DOCUMENT RESUME ED 302 420 TITLE 5, 1986. 88 97p. 1988 · 2014. 3. 24. · DOCUMENT RESUME ED 302 420 SE 050 258 TITLE Computers in Life Science Education. Volume 5, 1986. INSTITUTION

DOCUMENT RESUME

ED 302 420 SE 050 258

TITLE Computers in Life Science Education. Volume 5,1986.

INSTITUTION National Resource for Computers in Life ScienceEducation, Seattle, WA.

PUB DATE 88NOTE 97p.

AVAILABLE FROM Natlonal Resource for Computers in Life ScienceEducation, Mail Stop RC-70, University of Washington,Seattle, WA 98195 (Subscription $40.00).

PUB TYPE Collected Works - Serials (022)JOURNAL CIT Computers in Life Science Education; v5 n1-12 Jan-Dec

1988

EDRS PRICE MF01 Plus Postage. PC Not Available from EDRS.DESCRIPTORS *Biological Sciences; *College Science; Computer

Assisted Instruction; *Computer Uses in Education;Courseware; Higher Education; InstructionalImprovement; Microcomputers; *Newsletters; ScienceEducation; *Science Instruction; Secondary Education;*Secondary School Science; Simulation; TeachingMethods

ABSTRACT

Designed to serys as a means of communication amonglife science educators who anticipate or are currently usingmicrocomputers as an educational pool, this volume of newslettersprovides background information and practical suggestions on computeruse. Over 80 articles are included. Toxic areas include: (1) using apersonal computer in a plant physiology course; (2, annual report anddirectory; (3) Apple workstations; (4) hypercard; (5) expert systems;(6) computer graphics for simulations; (7) the use of computersimulations to reinforce laboratory experiences; (8) Stellasimulation software; (9) software evaluation; and (10) authoringsystems. Also provided are guidelines for the preparation andsubmission of articles to the newsletter, subscription information,indexes for each volume, and lists of meetings and synopses. "Where'sthe Software?" and "Keeping Abreast of the Literature" are periodicfeature articles. (CW)

*********************************************************************** Reproductions supplied by EDRS are the best that can be made ** from the original document. *

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O VOLUME S NUMBER 1, JANUARY 1988CN.1 I

CLSEE3 5(1)1-8, 1988 ISSN 0742-3233

I 11111111111111111101011111

HAROLD I. MODELLDepartment of RadiologyUniversity of WashingtonSeattle, Wasaingtm

MARCEL BLANCHAERDepartment of BiochemistryFaculty of MedicineUniversity of ManitobaWinnipeg, Manitoba

THEODORE J. CROVELLOGraduate Studies and ResearchCalifornia State University, Los AngelesLos Angeles, California

JAMES W. ECKBLADDepartment of BiologyLuther CollegeDecorah, Iowa

TSEGAYE HABTEMARIAMSchool of Veterinary MedicineTaskegee UniversityTuskegee, Alabruns

DONNA LARSCNSchool of NursingGrand Valley State CollegeAllendale, Michigan

TERRY M. MIKITENGraduate School of Biomedical SciencesUniversity of Texas Health ccience CenterSan Antonio, Texas

JAMES E. RANDALL=au of PhysiologyUniversity

Bloomington, Indiana

PATRICIA SCri:11111ANCollege of NursingOhio State UniversityColumbus, Ohio

RICHARD STULLCollege of PharmacyUniversity of ArkansasLittle Rod:, Arkansas

JAMES W. WOODSLister Hill National Center

for Biomedical CommunicationsNational Library If MedicineBethesda, Maryland

DOROTHY WOOLEY-McKAYDepartment of BiologyGlendale Community CollegeGlendale, Minna

GLEN YOSHIDADepartment of Life SciencesLos Angeles Southwest CollegeLos Angeles, California

NRCLSE

CONTENTS

"PERMISSION TO REPRODUCE THISMATERIAL IN MICROFICHE ONLYHAS SEEN GRANTED B

TO THE EDUCATIONAL RESOURCESINFORMATION CENTER (ERIC)."

FIRST STEPS USING PERSONAL COMPUTERSIN A PLANT PHYSIOLOGY COURSE

Larry Blakely

DIAGNOSTIC PRACTICE REINFORCESLESSONS FROM LABORATORIES

Nils S. Peterson, Stephen A. Feinerand David D. Barbee

1

FIRST STEPS USING PERSONALCOMPUTERS IN A PLANTPHYSIOLOGY COURSE

Larry BlakelyBiological Sciences Department, California State Polytechnic University,

U S. DEPARTMENT OF EDUCATIONOnce of Educatonat Research and Improve

EDUCATIONAL RESOURCES INFORMATCENTER IERICI

This document has been reproducedreceived from the person or Organilaonynnating rt

0 Mmor changes nave been made to ImoreprOduCtiOn quality

Pomona, California PointS Ohnewcr °Pinions stated in thiSdment do not necessarily reprez.ent ofOEM O0SrliOn Or policy

I have been snamored of the micro-computer since the days (ancient his-tory!) of the Osborne 1, my first micro.The microcomputer gradually becamethe indispensible tool for my researchand teaching in plant physiology. Iused the old ozzie (I called it "Porky"in honor of its oinky-sounding diskdrives) for obvious things like writingup lecture notes for class distribution,class handouts, and for keeping grades.I started to write tutorials ( not very ef-fective on Porky) and to dream abouthooking up Porky to lab equipment, butthen came the IBM-PC: goodbyeCP/M, hello color.

Microcomputers clearly promise tomake instruction in beginning and ad-vanced plant physiology courses moreeffective and more stimulating to stu-dents. All that is needed is the equip-ment, the time to get it all together andto write programs, and a A of imagi-nation a mighty tall order. A NSFgrant to a group of several members ofour department, including me, made itpossible for me to make a start. I nowhad an XT with a 10 Mb hard disk per-manently available in the teaching lab,plus 1 to 3 others that I could borrow,along, with an A/D boaid and a digitiz-ing tablet. Time to quit dreaming and

07423233/8840.00 + 200 fd 1988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENCE EDUCATION

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2 COMPUTERS IN LIFE SCIENCE EDUCATION, VOLUME 5, NUMBER 1, JANUARY 1988

get started.Since we began, the beginning one-

quarter course in plant physiology hasbeen given twice. The following sec-tions describe the major modificationsmade in the course to utilize micro-computers.

LABORATORY DATA ACQUISITIONAND ANALYSIS

I modified four laboratory exercises toutilize microcomputers for data acqui-sition (via either the serial port or aninternal AJD board) and data analysis.I also wrote simple user interfaces inBASIC. The four laboratories dealtwith transpiration, leaf temperatures,CO2uptake in photosynthesis andmeasuring pH changes in chloroplastsuspension.

TranspirationIn an exercise that I have used for sev-eral years, students measure the rate oftranspiration of 2-week-old bean seed-lings era-loying a weighing method.Data are collected on the effects of ananti-transpirant chemical and on theeffects of light versus darkness. Theplants that are used have two expandedleaves and are growing in a small pot.The total weight of each potted plant isless than 100 g (this is important be-cause of the limited capacity of the top-loading mg balance that is used).

Prior to taking transpiration rate mea-surements, the pots are sealed with tapeto prevent water loss from the soil, andany growth above the two expandedleaves is severed. Thus, essentially allwater loss is through the two leaves.The students determine the transpira-tion rate in units of mmol water lost perm2 of leaf area per second.

In previous years, leaf area was deter-mined by weighing cut-out paper leaftracings that were then compared to theweight of 0.01 m2piece of the samepaper. The rate of water loss was de-termined from weight measurementsmade at the beginning and end of a 30minute period.

Because of the time necesary for stu-dents to gain an accurate understandingof the written instructions once in thelab setting, and for actually carryingout the required steps of the experi-ment, it was necessary in the past to

assign different treatments to differentstudent teams. Each team posted itsresults on a blackboard at the end ofthe period, and at the next lab period,all students copied all data and pre-pared a report.

I realized that using microcomputerscould improve the efficiency of thisexercise in several ways. Data collec-tion and computations could be sub-stantially automated, prompts from thecomputer could keep the students ontrack, and final data reports could beaccurately prepared. Summary andinterpretation of data, done outside ofthe lab period, would still be the stu-dent's responsibility. With the in-creased efficiency, I found that eachteam could collect a set of data on allvariables. Class data were still com-bined for statistical analysis. The latestimplementation of this exercise utiliz-ing microcomputers is outlined below.

Equipment. Two stations were set up,each 'ith an IBM PC (with color mon-itor and one floppy disk drive), a print-er, and a top-loading mg balance con-nected to the PC via the serial port. Alarge cardboard wind deflector wasplaced around the balance so that aircurrents would not influence plantweight measurement.

For light conditions, three high inten-sity lamps were set up under whichplants were placed when not beingweighed. A small side room was usedfor dark conditions.

A digitizing tablet, attached to an-other PC, was set up at another station.The students had learned how to usethe digitizing tablet for measuring leafarea in a previous exercise.

Software. I wrote a QuickBASIC pro-gram to prompt the students for dataentry and to print data reports. Thisprogram ran continously on the com-puters at the weighing stations. Sigma-scan, a commercial software packagefor use with the digitizing tablet, wasused for determining leaf areas in m2.

Protocol. Each team of 2 students used2 plants, one of which they treated withan anti-transpirant chemical. The areaof the 2 leaves on each plant was deter-mined with the digitizer. After a per-

iod of adaptation under the lamps, theplants were taken to a weighing stationfor the intitial check-in. In response toprompts from the computer, the teammembers entered their team number,names, leaf areas of their plants, andthe name of the anti-transpirant theyused. They were then instructed toplace each plant, in turn, on the bal-ance. The computer recorded theweight from the balance and time fromthe computer's system clock. The pro-gram then stored the data in a disk file,using the students' team number andthe day's date (read from the systemclock) for the file name (for example,A50187.DAT, for team A5). The stu-dents then placed their two plants backunder the lamps.

After 10 to 15 minutes in the lightfollowing the first weighing, and whentheir weighing station computer wasfree, the team returned with the plantsfor a second weighing. The plantswere next dark-adapted for a few min-utes, and then weighings were made atthe beginning and end of 10-20 Min-utes in the dark. During each sessionwith the computer at their weighingstation, the student team's file wasopened and updated with the newweight and time data.

After making the final weighing, theteam requested a report of the resultsfrom the weighing station computer.The report included all the data theyhad entered (names, leaf areas, and leafresistances that they measured with aLi-Cor autoporometer) and the calcula-ted transpiration rates of each plant inlight and dark, based on weight, time,and leaf area data A report of all classdata was subsequently prepared (it in-cluded a statistical analysis of the data)and given to each student.

Results. The use of microcomputersenabled this lab execise to run moresmoothly than in previous years, andeach team obtained more data. Thestudents were impressed with the capa-bilities of the computer and appeared totake the exercise more seriously than inthe past.

Leaf temperaturesI have run a demonstration exercise

on the effects of radiation, wind speed,

©1988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE CCIENCE EDUCATION 0742-3233/88/S0.00 + 2.00

COMPUTERS IN LIFE SCIENCE EDUCATION, VOLUME 5, NUMBER 1, JANUARY 1988 3

and other factors on leaf temperature.One small thermocouple is pressedagainst the lower surface of a leaf,while another shielded thermocouple isused for air temperature. In the past,readings from both thermocoupleswere recorded on a two-pen chart re-corder. I decided to use the micro-computer for this exercise.

An IBM PC with a Metra-Byte A/Dboard (model Dash-8/Exp-16) wasused in place of the chart recorder.Thermocouples were attached directlyto the Exp-16.

In the first implementation of thecomputerized version of this exercise, Iused Labtech-Notebook Software. Forthe second, I wrote a BASICA pro-gram, that was easier to use and set up.In each case, a run-time graph of tem-peratures was displayed on the com-puter screen.

Leaf and air temperatures were re-corded under various conditions ofradiation and wind and saved in anASCII file. A screen dump of the com-puter screen on-line graph, or a graphproduced after the data was importedinto Lotus 1-2-3, was given to each stu-dent for inclusion in his or her reporton the demonstration. Graphing thedata on the screen using Lotus provid-ed an "instant replay" of the tempera-ture records.

The data was easier for the assem-bled class to see from the computerscreen display than from the chart re-corder used in the past. The ability tore-run a graph of the temperature ver-sus time data using Lotus made review-ing the results more dynamic. Further-more, setting up the demonstration iseasier using the computer than usingthe chart recorder.

Photosynthesis CO2 uptakeFor several years, I have used an exer-cise in which students make measure-ments of the rate of photosynthesisusing an infra-red gas analyzer and theCO2, depletion method. Briefly, a leafis placed in a small plastic box underlights. A 10 ml sample of gas is ex-tracted from the box with a syringe,and, after 1 to 4 minutes, another gassample is extracted. The gas samplesare injected into C01-free air passingthrough the sample port of the infra-red

gas analyzer. The COzcontent of eachsample is determined, and the differ-ence between the two calculated.Based on the volume of the box, thechange in CO2, content, the area of theleaf, and the elapsed time betweensample extractions, the photosynthesisrate can be calculated in micromols ofCO2 taken up per m1 per second.

I modified the exercise to include dataacquisition by a microcomputer. In thepast, students took data from a chart re-corder tracing, a process that was timeconsuming, prone to error, and requireda calibration and conversion from chartpaper units to ppm COr Use of themicrocomputer promised to eliminatethese problems. I did decide, however,to leave fmal calculation of photosyn-thesis rates up to the students. In thefuture, I will provide a program thatwill allow students to confirm their cal-culations, but still not do it for them!

The infra-red gas analyzer was con-,ted to a Metra-byte A/D board

(inodel Dash-8/Exp 16) on an IBM PC.C01-free air flowed through both thesample and reference ports of the infra-red gas analyzer. Students injected 10ml samples of air into the air flowinginto the sample port.

A digitizing tablet attached to anothermicrocomputer was also set out. Thiswas used for leaf area determination(as noted above, the students hadlearned to use it in earlier exercises).

I wrote a BASICA program that useda simple user interface to accept data,carry out calibration calculations, anddetermine the COicontent of samples.

One or more students were enlisted toinject calibration gases for the calibra-tion procedure. Each student team wasthen instructed to obtain a sample ofoutdoor air for CO2 content determina-tion. (I had them do this mainly to helpreinforce classroom discussions of theCO2 greenhouse effect, and implica-tions for plants in the future. We'rekeeping a year to year database of theresults.) Each team then obtained CO2measurements for photosynthesis deter-minations at 2 or 3 different light inten-sities. Finally, the CO2 compensationpoint of a particular species was deter-mined by each team.

Use of the A/D board and the micro-computer gave students the CO2con-

centration immediately after samplegas injection, helping to maintain theirinterest.

Photosynthesis - pH change inchlorwlast suspensionsAs part of an exercise on the light reac-tions of photosynthesis using suspen-sions of isolated chloroplasts, I conducta demonstration on change of the bath-ing solution pH upon illumination (anapparent result of proton uptake intothe chloroplast thylakoids). In the past,I used a chart recorder attached to asensitive pH meter to record changes inpH. I have now switched to using amicrocomputer and an A/D board inplace of the chart recorder. The bene-fits of using the microcomputer in thisexercise are similar to those mentionedabove in the case of the exercise onleaf temperatures.

LAB DATABASESFor several of the lab exercises used

in this course, I ask the students togather and analyze all of the data ob-tained by the whole class. In somecases, this is so that class averages,rather than just one data set, may beused for drawing conclusions. For ex-ample, in the case of the effect of auxinon the number of adventitious rootsformed on cuttings, there is consider-able variation from student team to stu-dent team. In other cases, this is doneto expand the number of species sam-pled. For example, in determinationsof the frequency (number per area) ofstomates on leaf surfaces, differentteams are asked to make counts ondifferent species.

In the past, I had the students posttheir team's data on the board, andwhen all data have been posted, allclass members were asked to copy allof the data for use in preparing reports.

I now use Lotus 1-2-3 custom work-sheets for this purpose. Student teamsenter their data, and when all class datahas been entered, each student has thecomputer print a summary. Lotus mac-ros were used to make friendlier userinterfaces for the worksheets. I will ac-cumulate data Lora year to year forcertain databases, for example, thestomate frequency versus plan speciesdatabase.

0742-3233/8840.00 + 2.00 0 1988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENCE EDUCATION


TUTORIALSI have written five BASICA microcom-puter turtorials to go with this course..They are available on the hard disk ofthe plant physiology lab PC and also inthe computer lab at the University'slibrary. Students may also copy theseonto their own personal disks, if theydesire.

I plan to write additional tutorials inthe future as time allows using BASICor other formats such as Show Partner.The latter promises to make tutorialwriting a much easier and more re-warding task, although I have just be-gun to explore its use.

The five tutorials include the fol-lowing.

Hangperson, a word game designedto foster familiarity with plantphysiology terminology.Transpiration Rate Tutorial, basedon mathematical models of planttranspiration, allows students toenter various parameters (such aswind speed, air humidity, and leafresistance) to see how they wouldaffect the transpiration rate.Spectrum, a tutorial to familiarizestudents with the spectrum of visiblelight, providing drill in equatingcolors with corresponding wave-length ranges.Hit the Reaction Center, a graphicsrepresentation of photon capture,energy transfer, and flourescence inthe light reactions of photosynthesis.The Calvin Cycle, a tutorial de-signed to give the student a famil-iarity with the overall process ofcarbon dioxide fixation in photo-synthesis and to gain familiaritywith the major intermediate sub-stances involved.

ADVANCED PLANT PHYSIOLOGYI used our microcomputers extensivelyduring the most recent offering of myone quarter course called AdvancedPlant Physiology. The students, whoare generally well motivated, are ex-pected to carry out more involved labprojects with less direction than is thecase in the beginning course. I decidedto have the students use Lotus 1-2-3extensively for data handling andgraphing, as I have found it very useful

for these purposes in my research.I introduced the students to this

spreadsheet software during the firstweek. I taught them the rudiments ofdata entry and graphing and encour-aged them to study the software man-ual to increase their expertise in usingthe package. In the various exercisesthey performed in subsequent weeks,they became adept at using Lotus 1-2-3for data handling and analysis and forpreparing tables of data and graphs fortheir reports. Of course, the studentslearned how to use the Metra-Byte A/Dboard and the digitizing tablet for usein their lab investigations (I do all thesetting-up in the beginning course).

CONCLUSIONSAlthough the full potential for usingmicrocomputers in plant physiologycourses is far from being realized, theexperiences described above have con-vinced me of the value of micros. Inevaluating the course, students oftenmention, in a positive way, their ex-perience with one or another of thecomputer applications, and they saythey would have liked more. Withtime and the acquisition of additionalequipment, the courses will surely bemade more stimulating learning experi-ences through the use of microcomput-ers. I want to write more tutorials,especially interactive ones. The soft-ware tools that are available for pro-grammers of limited sophistication(like me) are certainly adequate forwriting valuable, graphics-based tuto-rials. It just takes a lot of patience andimagination. I also want to extend thenumber of lab exercises using microsfor data collection and analysis. Theiruse should illustrate how things aredone in the real world, as well as beinstructional. I would like the studentsto be more independent in using themicros in the lab (a lot of hand-holdinghas been necessary so far). At somepoint in the future, I will require thateach student provide a floppy disk onwhich to store a set of programs andlab data

To anyone teaching courses like thiswho hasn't yet gotten into the world ofmicrocomputers, I would heartily rec-ommend that you do so. It will bring anew spark to your enjoyment of teach-

ing (although expect some periods offrustration!), and it will surely enhancethe educational experiences of yourstudents. I assure you that the possibil-ities are so numerous that you willnever, in a dozen careers, run out ofnew things to try in the quest for thebest ways of getting your educationalmessages across.

The tutorials described are available free toanyone who might like to try them. Theywere written in BASICA for a CGA cardand are GW-BASIC compatible. Pleasesend a stamped, self-addressed mailer and ablank 5 1/4" disk to: Larry Blakely, Biolog-ical Sciences Dept, California State Poly-technic University, Pomona, CA 91768.

SUBSCRIPTION INFORMATIONComputers in Life Science Education ispublished monthly by National Resourcefor Computers in Life Science Education,Mail Stop RC-70, University of Washing-ton, Seattle, WA 98195. Subscription rateis $30.00 for 12 issues, including postageand handling in the United States andCanada. Add $20.00 for postage (airmail)in Mexico and Europe and $23.00 for therest of the world.

This newsletter has been registered with theCopyright Clearance Center, Inc. Consentis given for copying of articles for personaland internal use. This consent is given onthe condition that the copier pay through theCenter the per-copy fee stated in the codeon the first page for copying beyond thatpermitted by the US Copyright Law.

Address orders, changes of address, andclaims for missing issues to NRCLSE, MailStop RC-70, Univ of Washington, Seattle,WA 98195. Claims for missing issues canbe honored only up to three months for do-mestic addresses and six months for foreignaddresses. Duplicate copies will not be sentto replace ones undelivered due to failure tonotify NRCLSE of change of address.

Address editorial correspondence to HaroldI. Modell, PhD, NRCLSE, Mail Stop RC-70, Univ of Washington, Seattle, WA98195.

(BITNET MODELL@UWALOCKE)

POSTMASTER: Send address changes toComputers in Life Science EducationNRCLSE, Mail Stop RC-70, University ofWashington, Seattle, WA 98195.

01988 BY NATIONAL RESOURCE FOR COMPUTERS INLIFE SCIENCE EDUCATION 0742-3233/88/30.00 + 2.00

5

COMPUTERS IN LIFE SCIENCE EDUCATION, VOLUME 5, NUMBER 1, JANUARY 1988 5

DIAGNOSTIC PRACTICE REINFORCESLESSONS FROM LABORATORIES

Nils S. Peterson, Stephen A. Feiner and David D. BarbeeDepartment of Computer and Information Science, University of Oregon, Eugene, Oregon andCollege of Veterinary Medicine, Washington State Unviersity, Pullman, Washington

In the past, simulations have beenwidely heralded as powerful learningvehicles. A large number of such mod-els have been created. A commonproperty of these models is interactionamong a large number of system prop-erties producing a range of responses insystem variables. Instructors usingthese simulations have used a varietyof strategies, ranging from non-directedsuggestions that students "play" to pre-scribed "experiments" with specifieddata recording, reporting, and analysis.Presumably, those teachers used simu-lations because they felt models wouldhelp students develop an appreciationfor system interactions, compensations,and limits of function. The success ofthat supposition remains largely con-jecture. As a modeler, I know that"playing" with a model develops my"feel" for it, but does the average stu-dent play as systematically?

The goal in giving students simula-tions is for them to build a frameworkfor integrating system interactions. Toachieve this, the learner must focus onpredicting model behavior, not watch-ing passively. One very successfulway to use a simulation is in smallgroup discussions where the teacherasks, "If I increase this parameter, canyou predict what will happen to thatvariable? How much? Why?" This iscause to effect reasoning. Certainly, itrepresents the type of mental dialog wehold with ourselves when exploring asystem. Prediction requires more effortand sophistication than watching, andstudents are less prone to adopt it.Consequently, most students truly playwith simulations rather than study theoperation predictively.Previously, we reported a study of

veterinary medical students in which,after a "traditional" lecture and wet labunit on the cardiovascular system, theystill held strongly anatomic views of

the cardiovascular system 3.4 Subse-quently we asked them to solve a seriesof "diagnostic" problems. These prob-lems were cast as games in which onecardiovascular property from a list of 8was disturbed randomly either up ordown. After playing 50 faults, we reas-sessed their perceptions of the system.Their views had developed a new func-tional dimension, and that new dimen-sion had shifted in the direction of anexpert's understanding.

Our diagnosis game helped studentspose questions that are the reciprocal ofthose described above. They were ask-ing, "If this variable has changed,which properties could explain it?"This is effect to cause reasoning. Ourprogram is called the Fault Identifica-tion Game. Like clinical experiences,the games reinforce both a functionalunderstanding of the specific systemand general problem-solving skills.This paper will show how new gamescan be written to compliment manyother simulations.

The data representations within thegame are very general. Problems canbe created for any system that can bedescribed in terms of properties (maxi-mum 20), variables (maximum 20) andthe interrelationships between them.We define "properties" as those entitiesthat are independent variables, that is,external inputs to the simulation. Our"variables" are dependent values thatarise from the interactions betweenproperties. The game is designed topermit non-programmers to developproblem sets easily. Creating a prob-lem requires entering data about a sys-tem into a specified format, using ei-ther a text editor or a spreadsheet.Required data are shown in Table 1.

AN ISOLATED HEART EXAMPLEIn the Isolated Heart preparation of theauthor's Ardiovascular Systems and

Table 1. Data required to create a problem.

For the problem:1. A short "presenting complaint"2. A short closing discussion

For each property and variable:1. Name, abbreviation, and units2. An order describing where (and if) each

will be displayed

For each property:1. A value2. The value's deviation from normal

(+,-,0)

For each variable:1. A flag describing the variable as either

quantitative or qualitative2. A deviation from normal (+,-,0), or

qualitative description (eg, "missing","open", "elevated")

Dynamics (CVSAD) program 4, fou.properties determine the performanceof the heart preload, afterload, heartrate and left heart pump state. If thevalues of these properties deviate fromnormal, the new state of the systemwill be reflected in values of its vari-ables: cardiac output, stroke volume,end systolic volume, end diastolicvolume, average arterial pressure, andpeak arterial pressure.

1r this example, one or more of theproperties was changed from normal.Some of the variables are abnormal,others show no change. In Figure 1,the player has requested values ofarterial pressure, cardiac output, enddiastolic volume and most recently,stroke volume. The game reports S V is22 ml and that this is normal (for adog). Some possible faults can beeliminated and some are clearly sug-gested. At this point, the player beginsto enter a tentative hypothesis, (Figure2, right). A hypothesis is recorded as a

0742- 3233/88/S0.00+ 2.00 0 1988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENCE EDUCATION


Ceneral berg Data Fault ID Options

uer rata

Ave Arterial Pres,

EliPeak Arterial Pres,

Cardiac Output

Peak Outflow

lad Diastolic Volume

Lid S stolic Volume

roe lo ume.

Fault ID Came

au t 1 Icahn

8 Preload

A Heart Rate

A Left Heart Pump Strength

8 Afterload

22.9 ml

9X

FIGURE 1. Querying data from the Fault Identification Game. Possible data arc shown onthe left. Possible faults are shown on the right.

setting (-,0,+) for each property. In thiscase, the diagnosis reads increased pre-load, decreased left heart strength, andothers normal.

When a diagnosis is entered, the com-puter can be asked to evaluate it. Inthis case, the answer is partly right, asshown in Figure 2, bottom. The re-sponse is reported as a Venn diagramshowing fault(s) suspected (S for sus-pected) by the player and fault(s) ac-tually contained in the problem (D fordisturbed). Verbal confirmation is alsoprovided. Correct answers show per-

feet intersection of sets "S" and "D",partial answers show partial intersec-tion, and wrong answers show no in-tersection. In this case another aspectof cardiac failure remains to bediagnosed.

AUTHORING A PROBLEMThe previous example comes from asimulation developed by the authors. Itis equally possible to create problemsfrom other simulations. It is equallypossible to create problems from othersimulations. It is even possible to cre-

General Query Data Fault ID Options Fault

1111111KMURIMMIE

Auer. Arterial Pres.

Peak Arterial Pres.

Cardiac Output

!Peak Outflow

End Diastolic Volume

I 'End Systolic Volume

f ,Stroke Volume

SUSPECTED SET:ALL correct

DISTURBED SET:SOME missing

IIIMIIMMI.71111111111

au t en i nation

+ Preload

8 Heart Rate

- Left Heart PUMP Strength

8 Afterload

FIGURE 2. Checking a diagnosis. The diagnosis is entered at the top left as high preload,low heart strength, others normal. The computer says the answer is partly correct, butsomething(s) is still missing.

ate useful student problems from amodel that is otherwise too complexfor instructional purposes. The centraltask is phrasing the problem with onlythose properties and variables that areappropriate to the student's academiclevel. The following examples showthe process of authoring a problem andsome of the diversisty of material thatcan be encoded.

The data for a problem are stored in acompacted file format. We have giventhese files DOS filename extensions.SDF, for System Data File. Problemauthors need not concern themselveswith the organization of SDF files,rather they may fill in textual forms ineither wordprocessor (ASCII) orspreadsheet (DIF) formats. In eachcase, a utility program converts SDFfiles to and from the more familiarformats. We chose wordprocessorsand spreadsheets as simple interfacesfaculty already have experience using.Further, they provide a potential hard-ware independent link for transferringfaults from one computer to anotherbecause the SDF format is machinedependent.

Encoding a clinical example with awordprocessorIn Pulmonary Pathophysiology, West5describes clinical observations fcr avariety of respiratory diseases. A faultproblem can be written. from hisdescription of restrictive disease(Chapter 5),

"Spirometry typically reveals a re-strictive pattern. The VC [Vital Capac-ity] is markedly reduced, but the gas isexhaled so rapidly that although theFEV02 [Forced Expiratory Volume in1 second] is low, the FEV/FVC%[Forced Vital Capacity] may exceed thenormal value. The square shape of theforced expiratory spirogram is instriking contrast to tile obstructivepattern. The MMFR [Maximum Mid-Expiratory Flow Rate] is normal orhigh. The flow volume curve does notshow the scooped out shape of obstruc-tive disease, and the flow rate is oftenhigher than normal when related toabsolute lung volume."5

"The arterial P02 and Pco2 are typi-cally reduced and the pH is normal.

©1988 BY NATIONAL RESOURCE FOR COMPUTERS LN LIFE SCIENCE EDUCATION 0742.3233/88/00.00+ 2.00

COMPUTERS IN LIFE SCIENCE EDUCATION, VOLUME 5, NUMBER 1, JANUARY 1988

Table 2. Data extracted from problem in West's Pulmonary Pathophysiology

Variable name Value Units % from normalForced Vital Capacity 2.9800 liters -38Forced Exp Vol -1 Sec 2.8800 liters -30FEVic/FVC 0.9700 tininess 13Max Midexpiratory Flow 4.7600 1/sec 6Peak Expir Flow 5.0000 1/sec -50

The hypoiemia is usually mild at restuntil the disease is advanced. How-ever, on exercise the Poe often fallsdramatically and cyanosis may beevident."5

The data in Table 2 were extractedfrom this description. These consti-tute the variables for the problem.The "faults" can be encoded in one oftwo ways. One set would focus onthe clinical description such as re-strictive disease or obstructive dis-ease. Alternately, the fault could beencoded in terms of defects in func-tional entities such as lung compli-ance and airway resistance.

Note that not all the diagnostic in-

Table 3. Encoding properties andvariables in a wordprocessor

1«variable 1»IForced Vital Capacity 1<name>IFVCI<abbr>I11<active var?>I 1=yes, make active in this

game11<index>I place data in cell 1 of

display-381<dev>IlIterskunits>101<binary var?I 0=no, its not binary dataII<display value ? >! 1=yes, display data in

the problem2.98001<value>I

1«property 1»ICompliancel<name>ICompll<abbr>III<active prop?>! 1=yes, show it in this

gamelkindex>I place this fault in

display position 1-11<qual value>I this property is below

normal01<balary prop?>I 0=no, its not binary0.00001<value>I actual value is unknown

from West's text, but itwon't be shown in thatgame, so its notnecessary

formation mentioned by West was used.The data selected are at the discretion ofthe problem's designer. However,adequate data must be present to distin-guish this fault from others in the sameproblem set.

Table 3 shows how the data from Table2 are entered into the wordprocessortemplate generated by the Fault Game'sutility. One data item is entered to theleft of the descriptions marked in singleangle brackets. Double bracketed termsindicate a division within the data recordand have no data associated with them.After entering all the data into this file, itis processed back into the compact SDFused by the game.

Programming a model into a spreadsheetPerhaps the most powerful method ofentering data is via a spreadsheet capableof writing DIF (Data InterchangeFormat) files. Most spreadsheet pro-grams have this capacity. In this ex-ample, Twin (Mosaic Software), a "workalike" of Lotus 1-2-3, was used. A filecalled FIGPLATE.WKS, provided withthe game, is a blank template to be filledwith author's data. The critical parts ofthe sheet are protected to preventinadvertent corruption of the format.

The equations for the model wereentered into the bottom of the sheet. Allthe functions and indirect referencingfeatures of the spreadsheet were used torelate the various properties and vari-ables. This master model can be savedto disk and reloaded. Faults are createdby altering desired properties and lettingthe model in the spreadsheet recalculatethe variables. Just a few key strokes arerequited to modify the sheet and save thedata for a new problem.

To augment a course in electronic trou-bleshooting, a circuit model was devel-oped using the schematic in Figure 3.Nine properties were used: battery inter-nal impedance, capacitor (dc) imped-ance, resistors R-1, R-2 and R-3, diode

131 6.3 volts Zb=0.IR1=511 R2,R3=10005101=5.0 uolt zenerCI=10 dtFM1=0-2 amps

FIGURE 3. Circuit diagram for an electriccircuit model that was developed in aspreadsheet and then had its data exportedto FIG game datasets.

impedance, DI, battery voltage, andthe total impedance of the externalcircuit. A portion of the spreadsheet(Table 4) shows the layout.

In building the model, some proper-ties, such as resistances and voltages,were direct analogs of the physicalcomponent they represented. Thefunctional impedance of the zenerdiode, however, was quite complex. Areverse biased zener diode exhibitshigh impedance (nearly an opencircuit) until the zener voltage isreached. From then on, it shows a volt-age dependent impedance thatmaintains the voltage drop across thediode at exactly the zener voltage.Using the logical @IF function of thespreadsheet, these two possibilitieswere modeled. When the fault pre-vented the voltage at TP2 from exceed-ing 5.0 volts, the diode impedance was1010 ohms (used for open circuits),otherwise an impedance was calculatedbased on current flow in the circuit toprovide zener action. Variables pre-sented to the student were limited tothe voltages present at the test points(relative to ground) and the current inMeter MI. Resistance measurementscould easily have been allowed, but thedesign goal for the model was to teachactive, in-circuit troubleshooting.

The model used rather complex for-mulas to compute the variables. For

0742-3233/88I$0.00+ 2.00 ©1988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENCE EDUCATION

8


example, the Test Point 1 voltage wascomputed from the battery voltage(Ebatt), the external system impedance(Rs), and the internal or battery imped-ance (Rb) using the followingformulas.

Total current(Itotal) = Ebatt/(Rs + Rb))Voltage of Test Point 1= Itotal * Rs.

In the normal circuit, the calculatedvoltage was 6.2743 volts. Faults wereeither shorts (R=0) or opens (R= 1010ohms) in the resis,ors, except for thebattery impedance which was merelyincreased to simulate a "weak" batterycondition. Only single fault sets wereused in this case, but multiple faultscould be used. A further refinementwould be to add ratings, such as power,to some components. The spreadsheetcould then evaluate to see if the in-structor induced fault caused an over-load somewhere else that "took out"additional components.

CUSTOM VERSUS GENERIC FAULTGAMESBoyle and Robinson described an acid-base balance game.' In this programthe student sees a graphical displayshowing arterial Et02, plasma bicar-bonate (HCO3 ), and pH. The normalrange, blood buffer line and currentpatient status are displayed graphicallyand in tabular form. The player isasked to determine the most appropri-ate diagnosis for the given acid-basestate. The screen then presents a list ofdiagnoses that represents all combina-tions of primary and compensatoy acid-base states. After a correct diagnosis isselected, the player can select treatmentoptions. These include defining valuesfor respiratory rate and tidal volumeand ordering an infusion of acid or ba-sic solution over a 60-minute intervalto adjust base excess or deficit. Thepatient's status is altered on the displayduring the course of therapy. Tutoringis provided for misdiagnoses and im-proper therapies.

noyle and Robinson's fault findinggame is a custom application for acid-base balance. The game described hereis generic. Acid-base problems couldbe implemented in the generic engine,but they would lose the graphical inter-face and the tutorial feedback of thecustom version. On the other hand,custom programs require new program-ming for each application. Our goalwas to develop an engine sufficientlyflexible that non-programmers coulduse it to create problem - solving exer-cises in a wide variety of domains withlittle instructor time investment.

AVAILABILITY OF THE PROGRAMA preliminary version of the programfor the IBM PC is available. The pro-gram uses the UCSD P-system and hassample problems along with studentand faculty manuals on disk. Thesemay be obtained by sending a blankfloppy disk and self addressed enve-lope (a diskette mailer is best) to theauthors. An improved version, used toillustrate this article is being preparedfor publication. Problem sets created

Table 4. Encoding a problem in a spreadsheet

for the initial version will IN. fully com-patible with the new one.

This work was supported by grants from USDepartment of Education (FTPSE) MG008440474and National Institutes of Health HLBI (SBIR)#1R43HL37790.

REFERENCES1. Boyle, Joseph III and Gloria Robinson.

Acid-base balance: An educationalcomputer game. BioScience 37(7):511-513, 1987.

2. Hopkins, RH, KB Campbell, and NSPeterson. Representations of perceivedrelations among the properties andvariables of a complex system. IEEETrans Sys. Man and Cyber. SMC-17(1),52-60, 1987.

3. Peterson, NS, KB Campbell, RHHopkins, and SA Feiner. An integratedcardiovascular tea:hing laboratory. ThePhysiologist 28:447448, 1985.

4. Peterson, NS and KB Campbell. Teach-ing cardiovascular integrations withcomputer laboratorirs. The Physiologist28:159 - 169,1985.

5. West, John B. Pulmonary Pathophysi-ology, 2nd ed. Williams and Wilkins,Baltimore, MD., 1982, p. 98.

«PROPERTIES»

<name><abbr>

<property 1>BatteryZbatt

<property 2>Impedance ClZcap-1

<property 3>Limit ResistorR-1

<active prop?> 1 1 1<index> 1 2 3<qual value> 0 0 0<binary prop> 0 0 0<value> 0.10000 10000000.0000 5

«VARIABLES»<variabie 1> <variable 2> <variable 3>

<name.> Test Point 1 Test Point 2 Test Point 3<abbr> E-TP I TP-2 TP-3<active var?> 1 1 1<index> 2 3<dev> 0 0 0<units> Volts Volts Volts<binary var?> 1 1 1<display value?> 1 1 1

<value> 6.2743 4.9899 2.4950

1988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENCE EDUCATION

9

0742-3233/88/50.00+ 2.00

VOLUME S NUMBER 2, FEBRUARY 1988 CLSEE3 5(1)9-16, 1988 ISSN 0742-3233

II :r 11111111111011111111111

HAROLD I. MODELLDepartment of RadiologyUm ?ashy of WashingtonSeattle, Washington

MARCEL BLANCIIAERDepartment of BiochemistryFaculty of MedicineUniversity of ManitobaWinnipeg, Manitoba

TIIEODORE J. CROVELLOGraduate Studies and ResearchCalifornia State University, Los AngelesLos Angeles, California


TSEGAYE IIABTEMARIAMSchool of Veterinary MedicineTuskegee UniversityTuskegee, Alabama

DONNA LARSONSchool of NursingGrand Valley State CollegeAllendale, Michigan

TERRY M. MIKITENGraduate School of Biomedical SciencesUniversity of Texas health Science CenterSan Antonio, Texas

JAMES E. RANDALLDepartment of PhysiologyIndiana UniversityBloomington, Indiana

PATRICIA SCIIWIRIANCollege of NursingOhio State UniversityColumbus, Ohio

RICHARD STULLCollege of PharmacyUniversity of ArkansasLittle Rock, Arkansas

JAMES W. WOODSLister I lill National Centerfor Biomedical Communications

National Library of MedicineBethesda, Maryland

DOROTHY WOOLEYMcKAYDepartment of BiologyGlendale Community CollegeGlendale, Arizona


NRCLSE

CONTENTS

NRCLSE ANNUAL REPORT FOR 1987 9

CLSE 1987-88 COLLEAGUE DIRECTORY PART I 11

IIIII...111! ImB

NRCLSE ANNUAL REPORTFOR 1987

The overall purpose of NRCLSE is tocultivate collaborative efforts amongfaculty with expertise in using com-puters in life science education. Thebroad goal of the Resource is fourfold:

1) to educate faculty in effective usesof computers in the curriculum;

2) to promote research aimed at eval-uating new applications of the com-puter to life science education;

3) to promote development of a crit-ical mass of high quality, versatilesoftware; and

4) to serve in a consultant capacity forlife science faculty currently activeor interested in becoming active inthis area.

This year's activities were focused onfurther establishing the Resource as aviable entity capable of serving the lifescience community. A statement ofNRCLSE's financial status is presentedin Table 1.

GENERAL OVERVIEW OFACTIVITIESIn 1987, NRCLSE applied for and re-ceived non-profit (501(c)(3)] statusfrom the Internal Revenue Service. Inaddition to making us eligible to re-ceive tax deductible donations, the IRSapproval of our non-profit status makesus eligible to apply to various founda-tions and funding agencies for supportof specific projects. We have begun aprocess to identify foundations whoseinterest areas include use of computersin life science education as an initialstep in developing future projects.

Publication of Computers in LifeScience Education continues to be ourprimary activity. However, during thepast year, we have continued our soft-ware development project in respira-tory physiology, provided informationto colleagues throughout the worldconcerning use of computers in lifescience curricula, participated inregional and national meetings, and

0742-3233/800.03 + 2.00 ©1988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENCE EDUCATION

10

10 COMPUTERS IN LIFE SCIENCE EDUCATION, VOLUME 5, NUMBER 2, FEBRUARY 1988

conducted demonstrations and work-shops at a number of institutions.

Computers in Life Science EducationSubscriptions to CLSE continue at thesame level as in previous years, withcInse to half of the subscriptions beingheld by libraries. The geographic dis-tribution of subscribers continues togrow. Distribution now includes sub-scribers in 10 countries representing 4continents.

In addition to our regular features,"Keeping Abreast of the Literature"and "Where's the Software?", we havebegun publishing an annual colleaguedirectory of life science educators in-terested in using or actively using thecomputer as a teaching tool. The intentin providing this information is to helplife science educators establish linkswith potential colleagues. Entries forthe directory have been drawn primar-ily from responses to the NRCLSEquestionnaires published in CLSE.

Software developmentWork on conversion of our Apple IIpackage of simulations (Simulations inPhysiology - The Respiratory System)to MS-DOS and Macintosh formatscontinued during this year. All threepackages arc now complete and avail-able for distribution. Appropriate an-nouncements and purchase informationfor each set was included in Computersin Life Science Education when eachset was completed.

Resource InformationDuring 1987, NRCLSE responded toover 75 requests for information con-cerning use of the computer as an edu-cational tool. The majority of theseconcerned suitable software for use inthe laboratory setting.

The geographic origin of the requestsindicates that we arc beginning to ac-complish our goal of providing wide-spread service. Requests originatedfrom institutions in Australia, Belgium,Canada, Denmark, England, Italy,Jamaica, Mexico, Spain, Sweden, andthe United States (24 states, the Districtof Columbia, the U.S. Virgin Islands,and Puerto Rico).

Table 1. NRCLSE Financial Report for year ending December, 1987.

Fund Balance, December 31, 1986

Revenues

$14,928

Cash donations 845Equipment donations 0Subscriptions 5,080Software sales 2.008

Total Revenues 7,933

ExpensesCLSE production 4,182.Operating supplies 2,102Contractual services 2,258Travel 567Software purchases 141

Total Expenses 9,250

Fund balance, December 31, 1987 $13,612

Presentations/ConsultationPersonnel from NRCLSE organized orparticipated in symposia, panels, andworkshops at 5 regional and nationalmeetings during the year. Workshopsand demonstrations were also present-ed at two universities. Topics includedteaching problem-solving in physiol-ogy, the computer as a teaching aid inbiological sciences, the role of thecomputer in the lecture hall, futuretrends in physiology teaching, andcomputer applications other thantraditional computer-aided instructionin health science education.

Establishing a peer critique mechanismfor softwareIn 1987, we began our effort to estab-lish a peer critique mechanism for re-viewing software by initiating a dialogon the topic in Computers in LifeScience Education. This effort willcontinue in 1988 with our first goalbeing to establish criteria for such amechanism. To get as much input aspossible before establishing specificcriteria, we will survey other groupsalready involved 3n peer review ofsoftware, and the two articles publishedin CLSE will be reprinted in Science

Software, a quarterly publication serv-ing the science community.

SPECIAL THANKSNRCLSE extends a very special"Thank you" to the following people,organizations, and institutions for theirsupport in 1987.

American Institute of BiologicalSciences730 11th Street, N.W.Washington, D.C. 2001-4584

Dr. Joel MichaelDepartment of PhysiologyRush Medical CollegeChicago, IL 60612

Paula Olcott6768 48th Ave. S?W.Seattle, WA 98136

University of WashingtonSeattle, WA 98195

Zenith Data Systems300 120th Ave.Bldg. 1, Suite 205Bellevue, WA 98005

1988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENCE EDUCATION 0742 - 3233/88/50.00+ 2.00

11

COMPUTERS IN LIFE SCIENCE EDUCATION, VOLUME 5, NUMBER 2, FEBRUARY 1988 11

CLSE 1987-88 COLLEAGUE DIRECTORY - PART I

The primary goal of the National Re-source for Computers in Life ScienceEducation (NRCLSE) is to cultivatecollaborative efforts among life sciencefaculty interested in using the computeras a teaching tool.

The directory that follows is updatedfrom the 1986-87 directory and wasdrawn primarily from respondents toquestionnaires printed in CLSE. It isintended to help rvadf.z-s identify col-

leagues with common interest areas.The listings arc arranged by the con-

tent areas identified in response to thequestion, "What content ar,as do youteach?" As at, alt, entries may appearunder more than one heading.

Although every attempt has beenmade to ensure that the information iscurrent and correct, it is likely thatsome errors appear in this list. Weapologize in advance for any inconve-

niences that may arise due to suchoversighcs. Part II of the directory willappear next month.

If yeir are aware of other colleaguesthat should be listed, please send theirnames, addresses, phone numbers, andteaching content areas to NRCLSE,Mail Stop RC-70, Univ. of Washing-ton, Seattle, WA 98195 or let us knowvia BITNET. NRCLSE's B1TNETaddress is MODELL@UWALOCKE.

AGRONOMYARNESON, PIIIL ADEPT OF PLANT PATHOLOGYCORNELL UNIVMIACA, NY 14853

BERGQI /1ST, BARTDEPT OF BIOLOGYUNIV NORTIIERN IOWACEDAR FALLS, IA 50614(319) 273.2723

UNIV OF MINNESOTAMORRIS, MN 56267(612) 5894211

GWLNN, DR 10ILN F

DEPT OF ANATOMYCASE WESTERN RESFIIVE

UNIVERSITYCI tIVELAND, 01144106(716) 368.2656(607)255.7871

PHILIP ADEPT OF BIOLOGYUNIV OF AKRON

DA V1S, THOMAS M COLLEGE OF VET MED AKRON. 011 44325 MESSICK. JOHN PDEPTS OF PLANT SCIENCE DRAWER V (216) 375.7160 DEPT OF BIOLOGYAND GENETICS MISS STATE UNIV. MS 39762MISSOURI SOUTilERN STATEUNIV OF NEW HAMPSHIRE (601) 325.1127 GWYN, DR D G COLI_EGEDURHAM. N1103824-3597 DEPT OF ANATOMY JOPLIN, MO 64801(603)862.3217 DICKINSON, WINIFRED DALHOUSIE UNIV (417) 625.9376

DEFT OF BIOLOGY I IALIFAX, NOVA SCOTIAESTES, DR GEORGE 0 UNIV OF STEUBENVILLE B3I141 CANADA ME YE.RS, PAUL EDEPT OF PLANT SCIENCE STEUBENVILLE, 01143952 (902)42 951 DEPT OF BI01. CIIEM &UNIV OF NEW I IAMPSIIIRI1 (614) 283.3771

STRUCTDURHAM, N1103824(603) 862.3720 DUGGAN, ELEANOR L

IIERRON AR Y ABIOMED LEARNING RES C111

TI111 CI IICAGO MED SC11001,3333 GREEN BAY RDBIOLOGY DEPT COLLEGE OP VET NIV.) NO RT1 CI IICAGO.11. 6006411111.SEL, ZANE R UNIV N CAROLINA AT TEXAS A&M UNIV (312) 578-3000 EXT 458DEPT OF AGRONOMY GREENSBORO COLLEGE STATION, Tx 77843

UNIV OF MISSOURI GREL 1BORO, NC 774121001 (409) 845.1780 MICKUS, 10ILNCOLUMBIA, MO 65211 (9191 379.5839DEPT OF BIOLOGY(3141882.2001 I IIWARD. STEPHEN ILLINOIS BENEDICTINE COLLELDER, DR BETTY DEPT OF BIOLOGY 5700 COLLEGE ROADANATOMY DEPT OF BIOLOGY BALDWINWALLACE LISLE, 11. 60532.0900BA SHOR, DAVID P GEORGIA SOUTHWESTERN' COLLEGE (312) 9604500 EXT 509DEPT OF BIOLOGY COLLEGE BEREA, 011 44017

UNIV N CAROLINA AT AMERICUS. GA 31709 (216) 826-7265 RALSTON, IICI IARLOTTE (912) 928.1250DEPT OF ANATOMY 5.1334CI IA RLOTTE, NC 28223 HOUSE. EDWIN W UNIV CALIF. SAN FRANCISCO(704) 597-4047 IERN'ER, JOAN W DEPT OF BIOLOGY 3RD & PARNASSUS AVESDEPT OF B'OLOGY IDAHO STATE UNIV SAN FRANCISCO. CA 94143BAUR. JOIN M 1110MAS MORE COLLEGE PO BOX 8007 (415) 476-1861DEPT OF BIOLOGY CRESTVIEW HILLS, KY 41017 POCATELLO, ID 83209

BRESCIA COLLEGE (606) 347.3374 208) 236.3765( ROBBINS, DR HERBERT C120 WEST SEVENTH STDALLAS BAPTIST UNIVOWENSBORO, KY 42.A1 NKELSTEIN, MICHAEL HOUSE, DR HERBERT W 7777 WEST KIEST BLVD(5011685.3131 EXT 276 DEPT OF ORAL PATHOLOGY ELON COLLEGE DALLAS, TX 75211.980COLL7GE OF DENTISTRY BOX 7270 (214) 333.5302BELLMER, SISTER RIZA- UNIV OF IOWA ELON COLLEGE. NC 27244

11E11111 IOWA CITY. IA 52242 (919) 5844.94 SINNA MON, WALTTRe:ITY COLLEGE (319) 353.7370CENTRAL. WESLEYANWASHINGTON, DC 20017 JONES, NaritAELE COLLEGE, BOX 443(202) 939.5190 FREED. DR JAMES M DEPTS OF &: CENTRAL, SC 29630

DEPT OF ZOOLOGY FLINDERS UNIV OF SOTTII I (803) 639.2453 !XT 356BEN'NEIT, THOMAS F 011TO WESLEYAN UNIV AUSTRALIADEPT OF BIOLOGY DELAWARE, 01143015 SCHOOL OF MEDICINE SMALL. JAMES WBELLARMINE COLLFGE (614) 3694431 EXT430 BEDFORD PARK. 5042 DEFT OF BIOLOGYNEWBURG ROAD AUSTRALIA ROLLINS COLLEGE, BOX 2643LOUISVILLE, KY 4010:: GOOCII, VAN D

WETTER PARK. F1.32789(502) 4528198 DEFT OF SCI & MATH KEEFE, J RICHARD (305) 6442433

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SPRAGUE, RUTH M DEPT OF BIOLOGY JOHNSON, WALTER FURMAN UNIVDEPTS OF ANATOMY k ANDREWS UNIV NW COLLEGE OF CHEROFRACIIC GREENVILLE, SC 29613NEUROBIOLOGY BERRIEN SPRINGS, MI 49104 2501 W 84TH ST (803) 294-3249UNIV OF VERMONT COLLEGE (616) 471-3243 BLOOMINGTON, MN 55431OF MEDICINE(612) 888-4777 OCT 290 SUELTER, CLARENCE HBURLINGTON, VT 05405 ANIMAL SCIENCE

DEPT OF BIOCHEMISTRY(802) 656-0416 KENNELLY, DR JOHN J KIRBY, DR EDWARD MICHIGAN STATE UNIVDEPT OF ANIMAL SCIENCE DEPT OF BIOCHEMISTRY EAST LANSING, MI 48824WILHELM, DALLAS UNIV OF ALBERTA TEMPI E UNIV IH.TII SCI CTR (517) 355-1708DEPT OF BIOLOGY EDMONTON. ALBERTA 3420 N BROAD STHASTINGS COLLEGE CANADA T6G 2P5 PHILADELPHIA, PA 19140 TESKEY, SISTER NANCYHASTINGS, NE 68901 (403) 432-2133 (215) 221-4180 HOLY NAMES COLLEGE(402) 463.2402 EXT 2643500 MOUNTAIN BLVDBIOCHEMISTRY KRUSE, ANNA C OAKLAND, CA 94619WILLIAMS, STANLEY C BATES, WILLIAM K CABRINI COLLEGE (415) 436-0111DEPT OF BIOL SCIENCES DEPT OF BIOLOGY RADNOR, PA 19087SAN FRANCISCO STATE UNIV UNIV NORTH CAROLINA (215) 687-2100 TREBLE, DONALD H1600 HOLLOWAY AVE GREENSBORO, NC 27412DEPT OF BIOCHEMISTRYSAN FRANCISCO, CA 94132 (919) 379-5391 MASSEY, K LORNE ALBANY MEDICAL COLLEGE

DEPT OF PATHOLOGY ALBANY, NY 12208WILSON, DR MARLENE BLANCHAER, MARCEL COLLEGE OF MEDICINE (518) 445-5364UNIV OF PORTLAND DEPT OF BIOCHEMISTRY UNIV SASKATCHEWAN5000 N WILLAMETTE BLVD FACULTY OF MEDICINE SASKATOON, WAGNER, MARTIN JPORTLAND, OR 97203 UNIV OF MANITOBA SASKATCHEWAN DEPT OF BIOCHEMISTRY(503) 283.7123 WINNIPEG, MANITOBA CANADA S7N OWO BAYLOR COLLEGE OFCANADA R3E 0W3DENTISTRYZETTIER, SHIRLEY (204) 788.6570 NORRIS, THOMAS E 3302 GASTON AVEDAVENPORT COLLEGE

SENIOR ASSOC DEAN DALLAS, TX 75246415 EAST FULTON COOK, DAVID E MOREHOUSE SCH OF MED (214) 828-8261GRAND RAPIDS, MI 49503 DIV SCI & MATHEMATICS 720 WE3TVIEW DR SW(616) 431-3511 DAKOTA STATE COLLEGE ATLANTA, GA 30310-1495 WINTER, CHARLES GMADISON, SD 57042-1799DEPT OF BIOCHEMISTRYANIMAL BEHAVIOR (605) 252-5194 PEARSON, ANDREW UNIV OF ARKANSASAMLANER, CHARLES J JR

DEPT OF BIOCHEMISTRY COLLEGE OF MEDICINEDEPT OF ZOOLOGY CREUT7 CHARLES UNIV WEST INDIES 4301 W MARKHAM STUNIV OF ARKANSAS DEPT OF BIOLOGY MONA, KINGSTON 7 LITTLE ROCK, AR 72205-7199FAYETTEVILLE, AR 72701 UNIV TOLEDO JAMAICA (501) 661-5190TOLEDO, 011 43606 (809) 927-2290ASHTON, GEOFFREY C (419) 537-4159BIOLOGYDEPT OF GENETICS

PETERSON, JULIAN A BASHOR, DAVID PUNIV OF HAWAII DICKINSON, WINIFRED DEPT OF BIOCHEMISTRY DEPT OF BIOLOGY1960 EAST-WEST ROAD DEPT OF BIOLOGY UNIV TEXAS HEALTH SCI CTR UNIV N CAROLINA ATHONOLULU, HI 96822 UNIV OF STEUBENVILLE 5323 HARRY HINES BLVD CHARLOTTE(808) 948.8552 STEUBENVILLE, 011 43952 DALLAS, TX 75235.9038 CHARLOTTE, NC 28223(614) 283.3771 (214) 688-2361 (704) 597-4047ROGER GAMES,DEPT OF BIOL SCIENCE EUSTIS-TURF, ELIZABETH RAWITCH, ALLEN B BAUR, JOHN MCAL POLYTECH STATE UNIV MEDICAL COLLEGE OF DEPT OF BIOCHEMISTRY DEPT OF BIOLOGYSAN LUIS OBISPO, CA 93407 VIRGINA UNIV KANSAS MED CTR BRESCIA COLLEGE(805) 546-2551 BOX 694, MCV STATION RAINBOW BLVD AT 39111 120 WEST SEVENTH STRICHMOND, VA 23298 KANSAS CITY, KS 66103 OWENSBORO, KY 42301HECKENLIVELY, DONALD B (804) 786-9824 (913) 588-6957 (502) 685.3131 EXT 276DEPT OF BIOLOGY

HILLSDALE COLLEGE FERNER, JOHN W SCHADLER, DANIEL L BERG, VIRGINIAIIILLSDALE, MI 49242 DEPT OF BIOLOGY DEFT OF BOUM DEPT OF BIOLOGY(517) 437-7341 THOMAS MORE COLLEGE OGLETHORPE UNIV UNIV OF NORTHERN IOWACRESTVIEW HILLS, KY 41017 4484 PEACHTREE ROAD NE CEDAR FALLS, IA 50614KESLER, DAVID H (606) 344.3374 ATLANTA, GA 30319.2797 (319)273.2770DEPT OF BIOLOGY(404) 261-1441 EXT 403RHODES COLLEGE FORRESTER, JOE

BOWKER, LESLIE S2000 NORTH PARKWAY DEPT OF BIOCHEMISTRY SOIL, DIETER DEPT OF BIOL SCIENCEMEMPHIS, TN 38112 UNIV OF MISSOURI DEPT OF MOLECULAR CAL POLYTECH STATE(901) 726-3557 M121 MED SCI BLDG BIOPHYS AND BIOCHEM UNIVERSITYCOLUMBIA, MO 65212 YALE UNIV SAN LUIS OBISPO, CA 93407LA BAR, MARTIN (314) 882-8795 260 WHITNEY AVE (805) 3462788CENTRAL WESLEYANNEW HAVEN, CT 06511COLLEGE HARLEY, CALVIN B (203) 436-3611 BROADWAY, RUBY LCENTRAL, SC 29630 DEPT OF BIOCHEMISTRY

DEPT OF NATURAL SCIENCE(803) 639.2453 McMASTER UNIV SPENCER, TREVOR DILLARD UNIVERSITYHAMILTON, ONTARIO DEPT OF BIOCHEMISTRY NEW ORLEANS, IA 70122ODE, PHILIP CANADA UN 375 QUEENS UNIVERSITY (504) 283-8822 EXT 746DEPT OF BIOLOGY (416) 525.9140 EXT 2040 KINGSTON ONTARIOTHIEL COLLEGECANADA K7L 3N6 CARICO, JAMES EGREENVILE, PA 16125 HOUSE, DR HERBERT W (613) 545-2997 DEPT OF BIOLOGY(412) 588-7700 ELON COLLEGE, BOX 2270

LYNCHBURG COLLEGEELON COLLEGE, NC 27244 STRATTON, LEWIS P LYNCHBURG, VA 24501STOUT, JOHN (919)584.2294 DEPT OF BIOLOGY (804) 522-8366

©1988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENCE EDUCATION 0142-3233/88/50.00 -I- 200

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CHEW. FSDEPT OF BIOLOGYTUFTS UNIVMEDFORD, MA 02155(617) 381-3189 EXT 3195

CHIEN. PAUL KDEPT OF BIOLOGYUNIV OF SAN FRArCISCOSAN FRANCISCO. CA 94117(415% 666-6345

COCKERHAM, BILLFRESNO PACIFIC COLLEGE1717 S CHESTNUTFRESNO, CA 93702(209) 453.2045

COLLINS. DR MICHAEL A JDEPT OF BIOLOGYMEMORIAL UNIV OFNEWFOUNDLAND

STJOHNS,NEWFOUN'DLANDCANADA AI B 3X9(709) 737-8031

DILLEHAY, DR JANEDEPT OF BIOLOGYGALLAUDET COLLEGE800 FLORIDA AVE NEWASHINGTON, DC 20002(202) 651-5531

DOVE, LEWISDEPT OFBIOL SCIENCESWESTERN ILLINOIS UNIVMACOMB, IL 61455(309) 293-1166

DUGGAN. ELEANOR LBIOLOGY DEFTUNIV OF N CAROLINA AT

GREENSBOROROOM 309, EB ERHART BLDGGREENSBORO, NC 27412-5001(919) 379-5839

EILEUS, LAWRENCE JDEPT OF BIOLOGYUNIV OF NORTHERN IOWACEDAR FALLS, IA 50614(319) 273.2218

FANNING, MARSHA EDEPT OF BIOLOGYLENOIR-RHYNE COLLEGEHICKORY, NC 28603(704) 323-7270

FERNER. JOHN WDEPT OF BIOLOGYTHOMAS MORE COLLEGECRESTVIEW HILLS, KY 41017(606) 344-3374

FONTANE, DR ARDEPT OF BIOLOGYUNIV OF VICTORIAPO BOX 1700VICTORIA, BC V8W 2Y2CANADA(604) 721-7131

FREY, DENNISDEPT OF BIOL SCIENCECAL POLYTECH STATE

UNIVERSITY

SAN LUIS OBISPO, CA 93407(805) 546-2802

FROLA, JOHN LDEPT OF BIOLOGYUNIV OF AKRONAKRON. OH 44325(216) 375-7153

GIBSON, LINDADEPT OF BIOLOGYEASTERN WASH UNIVCHENEY. WA 99004(509) 359.2345

GORMAN. MARKBALDWIN-WALLACE COLLBEREA, OH 44017(216) 826-2217

HAMAN, ACDEPT OF BIOLOGYUNIV NORTHERN IOWACEDAR FALLS, IA 50614(319) 273-2726

HECKENLIVELY, DONALD BDEPT OF BIOLOGYIITLLSDALE COLLEGEHILLSDALE. MI 49242(517) 437-7341

ITEDMAN, STEPHEN CDEPT OF BIOLOGYUNIV OF MINN AT DULUTH2400 OAKLAND AVEDULUTH, MN 55812(218) 726-8122

HEIMBROOK, MARGARET EDEPT OF BIOL SCIENCESUNIV OF NORTHERN

COLORADOGREELEY, CO 80639(303) 351-2644

HODGSON, DR LYNN MNORTHERN STATE COLLEGEBOX 673ABERDEEN, SD 57401(605) 222-2432

HOUSE, EDWIN WDEPT OF BIOLOGYIDAHO STATE UNIVPO BOX 8007POCATELLO. ID 83209(208) 2363765

HOUSE, DR HERBERT WELON COLLEGEBOX 2270ELON COLLEGE, NC 27244(919) 584-2294

HUMORA, PAULDEPT OF BIOLOGYNORTH ADAMS ST COLLEGENORTH ADAMS, MA 01267(413) 664-4511 EXT 343

JANKAY, PETERDEPT OFBIOL SCIENCECAL POLYTECH STATE

UNIVERSITYSAN LUIS OBISPO, CA 93407(805) 5462360

JOHNSON. J RONALDDEPT OF BIOLOGYMIDLAND COLLEGEFREMONT, NE 68025

KESSLER, SISTER IRMADEPT OF BIOLOGYCOLLEGE OF ST ELIZABETHCONVENT STATION, NJ 07961(201) 539-1600

KLEINSIVITH, LEWIS JDEPT OF BIOLOGYUNIV OF MICHIGAi4ANN ARBOR. MI 48109-1048(313) 763-3290

KRUSE, ANNA CCABRINI COLLEGERADNOR, PA 19087(215) 687-2100

LEISMAN, DR GILBERT ADEPT OF BIOLOGYEMPORIA STATE UNIVEMPORIA, KS 66801(316) 343-1200 EXT 5617

McCALLEY, DAVID VDEPT OF BIOLOGYUNIV NORTHERN IOWACEDAR FALLS, IA 50614(319) 273-2581

McGIVERN, MARY JEANDEPT OF BIOLOGYGEORGIAN COURT COLLEGELAKEWOOD. NJ 08701(201) 364-2200 EXT 345

MELE, FRANK MDEPT OF BIOLOGYJERSEY CITY STATE

COLLEGEJERSEY CITY, NJ 07305-1597'01) 547-3054

ODE, PHILIPDEPT OF BIOLOGYTHIEL COLLEGEGREENVTLE, PA 16125(412) 588-7700

ORE. ALANDEPT OF BIOLOGYUNIV NORTHERN IOWACEDAR FALLS, IA 50614(319) 273 -2150

OWEN, LAWTONKANSAS WESLEYAN100 E CLAFLDISAUNA, KS 67401(913) 827-5541

PIPERBERG, JOEL BDEPT OF BIOLOGYSIMMONS COLLEGE300 THE FENWAYBOSTON, MA 02115(617) 738-2196

POLLOCK, LWDEPT OF BIOLOGYDREW UNIVERSITYMADISON, NJ 07940(201) 377-3000 EXT 358

QUINN:DAVID LDEPT OF BIOLOGYMUSKINGUM COLLEGENEW CONCORD. OH 43762(614) 826-8225

RALPH, CHARLES LDEPT OF ZOOLOGYCOLORADO STATE UNIVFT COLLINS, CO 80523(303) 491-6105

RALSTON. HJDEPT OF ANATOMY S-1334UNIV CALIFORNIA AT SAN

FRANCISCOSAN FRANCISCO, CA 94143(415) 476-1861

RENISH. ALMA JEANUNIV OF WISCONSIN

PARKSIDEBOX 2000KENOSHA, WI 53141-2000(414) 553-2213

ROBBINS. DR HERBERT CDALLAS BAPTIST UNIV7777 WEST KIEST BLVDDALLAS. TX 75211.9800(214) 333-5302

RUNYAN, MICHAEL EDEPT OF BIOLOGYLANDER COLLEGEGREENWOOD, SC 29646(803) 229.8385

SAVAGE, WAYNEDEPT OF BIOLOGYSAN JOSE STATE UNIVSAN JOSE, CA 95192(408) 277-2355

SCHADLER, DANIEL LDEPT OF BIOLOGYOGLETHORPE UNIV4484 PEACHTREE ROAD NEATLANTA, GA 30319-2797(404) 261-1441 EXT 403

SCHNEIWEIS S. JEANNETTE WDEPT OF BIOLOGYHOFSTRA UNIVHEMPSTEAD, NY 11550(516) 560.5521

SCHWARTZ, ORLANDO ADEPT OF BIOLOGYUNIV NORTHERN IOWACEDAR FALLS, IA 50614(319) 273-2106

SHOLES, OWEN DVDEPT OF NATURAL SCIENCESASSUMPTION COLLEGE500 SALISBURY STWORCESTER, MA 01609(617) 752-5615 EXT 257

SINGLETARY, ROBERT LDEPT OF BIOLOGYUNIV OF BRIDGEPORTBRIDGEPORT, CT 06601(203) 576-4265

SINNAMON, WALT

0742-3233/88/50.00 + 2.00 1988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENCE EDUCATION

14


CENTRAL WESLEYAN TURNELL, MJ WESSEL-BEAVER, LINDA WARRENSBURG, MO 64093COLLEGE DEPT OF ZOOLOGY DEPT OF AGRONOMY AND (816) 429-4933BOX 443 UNIVERSITY OF ALBERTA SOILSCENTRAL, SC 29630 EDMONTON, ALBERTA COLLEGE OF AGRICULTURAL DICKINSON, WINIFRED(803) 639.2453 EXT 356 CANADA T6G 2E9 SCIENCE DEPT OF BIOLOGY

(403) 432-0665 UNIV OF PUERTO RICO UNIV OF STEUBENVILLESICAVARIL, DR RUSSELL V MAYAGUEZ, P R 00708 STEUBENVILLE, 011 43952DEFT OF GENETICS VERCII, RICHARD (809) 834-4040 EXT 2566 (614) 223-377101110 STATE UNIV DEPT OF BIOLOGY961 BIOL SCI BLDG NORTHLAND COLLEGE WILLIAMS, STANLEY C DOYLE, JAMES484 WEST TWELFITI AVE ASHLAND, WI 54806 DEPT OF BIOL SCIENCES DEPT OF BIOLOGYCOLUMBUS. 011 4321 0 (715) 682-4531 EXT 335 SAN FRANCISCO STATE UNIV AQUINAS COLLEGE(614) 422-1310 SAN FRANCISCO, CA 94132 GRAND RAPIDS, MI 49506

VOORHEES. FRANK RAY (616) 459-8281 EXT 387SMALL. JAMES W DEPT OF BIOLOGY BIOPHYSICSDEPT OF BIOLOGY CENTRAL MISSOURI STATE AMLANER, CHARLES I, JR GIBSON, LINDAROLLINS COLLEGE UNIV DEPT OF ZOOLOGY DEPT OF BIOLOGYBOX 2643 WARRENSBURG, MO 64093 UNIV OF ARKANSAS EASTERN WASHINGTON UNIVWINTER PARK, FL 32789 (816) 429-4933 FAYETTEVILLE, AR 72701 CHENEY, WA 99004(305) 646-2433

(509) 359-2845WALLACE, ROBERT L RAM, JEFFREY L

SOBIESKI. ROD DEPT OF BIOLOGY DEPT OF PHYSIOLOGY HODGSON, DR LYNN MDEPT OF BIOLOGY RIPON COLLEGE WAYNE STATE UNIV SC11 NORTHERN STATE COLLEGEEMPORIA STATE UNIV RIPON, WI 54971 MEDICINE ABERDEEN, SD 57401EMPORIA, KS 66801 (414) 748.8122 DETROIT, MI 48201 (605) 222-2432(316) 343-1200 EXT 5620 (313) 577-1558

WALTERS, DIRK R HOLLAND, V LSTANSFIELD, WILLIAM DEPT OF BIOLOGY SACHS, DR FREDERICK DEPT OF BIOL SCIENCEDEPT OF BIOL SCIENCE CAL POLYTECH STATE UNIV DEPT OF BIOPHYSICAL SCI CAL POLYTECII STATE UNIVCAL PnLYTECH STATE UNIV SAN LUIS OBISPO, CA 93407 SUNY AT BUFFALO SAN LUIS OBISPO, CA 93407SAN LUIS OBISPO, CA 93407 (805) 546-2721 BUFFALO, NY 14214 (805) 546-2789(805) 546-2875 (716) 831-3289WEISS, DR EDWARD HOLM, F ASTANTON, GEORGE E CHRISTOPHER NEWPORT SLAYMAN, C L DEPT OF CROP SCI & PLANTDLFT OF BIOLOGY COLLEGE DEPT OF PHYSIOLOGY ECOLOGYCOLUMBUS COLLEGE 50 SHOE LANE YALE SCHOOL OF MEDICINE UNIV OF SASKATCHEWANCOLUMBUS, GA 31993 NEWPORT NEWS, VA 23606 NEW HAVEN, a 06510-8026 SASKATOON, SK SIN OWO(404) 568.2065 (804) 599 -7044 (203) 788-4478 CANADA

(306) 966-5006STEIN, PAUL WHALLS, M.ARVIN WUNDER, CHARLES CDEPT OF BIOLOGY DEPT OF BIOL SCIENCE DEPT OF PHYSIOLOGY JANKAY, PETERWASHINGTON UNIV CAL POLYTECH STATE UNIV UNIV OF IOWA DEPT OF BIOL SCIENCEST LOUIS, MO 63130 SAN LUIS OBISPO, CA 93407 OAKDALE CAMPUS CAL POLYTECH STATE UNIV(314)889.6824 (805) 546-1357 OAKDALE IA 52319 SAN LUIS OBISPO, CA 93407

(319) 353-4704 (805)546.2860STEUCEK, DR GUY L WILHELM, DALLASDEPT OF BIOLOGY DEPT OF BIOLOGY BIOSTATISTICS KEIL, DAVID JMILLERSVILLE UNIV HASTINGS COLLEGE HECKENIIVELY, DONALD B DEPT OF BIOL SCIENCESMILLERS VILLE, PA 17551 HASTINGS, NE 58901 DEPT OF BIOLOGY CAL POLYTECH STATE UNIV(/17) 872-3339 (402) 463.2402 EXT 264 HILLSDALE COLLEGE SAN LUIS OBISPO, CA 93407

HILLSDALE, MI 49242 (805)546.2043STONE, LARRIE E BIOMETRICS (517) 437-7341DANA COLLEGE BEAVER, JAMES S LA BAR, MARTINBLAIR, NE 68008 DEPT OF AGRONOMY AND PETERSON, MARGARET GE CENTRAL WESLEYAN COLL(402) 426-4101 EXT 229 SOILS BIOSTATISTICS RES C112 CENTRAL, SC 29630

COLLEGE OF AGRICULTURAL UNIV OF CONNECTICUT (803) 639-2453STRATTON, LEWIS P SCIENCE HEALTH C112 AM034DEPT OF BIOLOGY UNIV OF PUERTO RICO FARMINGTON, a 06062 ROBBINS, DR HERBERT CFURMAN UNIV MAYAGUEZ, P R 00708 (203) 674-3834 DALLAS B XVIIST UNIVGREENVILLE, SC 29613 (809) 834.4040 EXT 2380 DALLAS, TX 75211-9800(803)294.3249 SAKTI, JANA (214) 333-5302

BOYNTON, DR JOHN E DEPT OF CROP SCIENCESWEET, HAVEN ST JOSEPH'S COLLEGE UNIV OF SASKATCHEWAN RUNYAN, MICHAEL EDEPT OF BIOLOGY 155 ROE BLVD SASKATOON, SK s7N OWO DEPT OF BIOLOGYUNIV OF CENTRAL FLORIDA PATCHOGUE, NY 11772 CANADA LANDER COLLEGEORLANDO, FL 32816 (516) 654-3200 (306) 966-4952 GREENWOOD, SC 29646(305) 275-2922

(803)229.8385STEUCEK, DR GUY L BOTANY

TAUB, STEPHAN R DEPT OF BIOLOGY BLACK, JOE B SAVAGE, WAYNEDEPT OF BIOLOGY MILLERSVILLE UNIV DEPT OF BIOLOGY DEPT OF BIOLOGYGEORGE MASON UNIV MILLERSVILLE, PA 17551 LOUISIANA COLLEGE SAN JOSE STATE UNIVFAIRFAX, VA 22030 (717) 872-3339 PINEVILLE, LA 71359 SAN JOSE, CA 95192(703) 323-2181 (318)487 -7611 (408)277.2355

WATROUS, JAMESTESKEY, SISTER NANCY DEPT OF BIOLOGY CASTANER, DAVID SCHADLER, DANIEL LHOLY NAMES COLLEGE ST JOSEPHS UNIV DEPT t. BIOLOGY DEPT OF BIOLOGYOAKLAND, CA 94619 PHILADELPHIA, PA 19131 CENTRAL MISSOURI STATE OGLETHORPE UNIV(415)436-0111 (215) 879-7342 UNIVERSITY 4484 PEACHTREE ROAD NE

0 1988 BY NATIONAL RESOURCE FOR COMPUTERS IN T-iFE SCIENCE EDUCATION 07423233/88/30.00 + 2,00


ATLANTA, GA 30319.2797 RALSTON, H DEPT OF PLANT SCIENCE BOX 3-406 JIIIVEIC(404) 261-1441 EXT 4 03 DEPT OF ANATOMY S-1334 UNIV OF ARIZONA GAINESVILLE, FL 32610

UNIV OF CALIFORNIA, SAN BUILDING #36 (904) 392.2674SHAH M FRANCISCO TUCSON, AZ 85721DF.PT OF SCIENCE & MATII 3RD & PARNASSUS AVES (607.) 621-1257 WETSTONE, SCOTTRUST COLLEGE SAN FRANCISCO, CA 94143 UNIV OF CONNECTICUTHOLLY SPRINGS, MS 38635 (415) 476-1861 KREIDER, DR DAVID L HEALTH CTR AM033(601) 2524661 DEPT OF ANIMAL SCIENCES FARMINGTON, Cr 06032

SAVAGE, WAYNE UNIV OF ARKANSAS (203) 674-3336SNOW, MICHAEL D DEPT OF BIOLOGY FAYETTEVILLE, AR 72701DEPT OF PINS & LIFE SCI SAN JOSE STATE UNIV (501) 575-4870 ZAJICEK, GERSHOMUNIVERSITY OF PORTLAND SAN JOSE, CA 95192 MEDICAL COMPUTER500 N WILLAMETTE BLVD (408) 277-2355 KRULL, DR SARA HEBREW UNIVERSITY-PORTLAND, OR 97203 SCHOOL OF NuRsuG HADASSAII MEDICAL(503) 283.7175 STENROOS, ORRIES 0 ST LOUIS UNIV MED CTR SCHOOL

BIOLOGY DEPARTMENT ST LOUIS, MO 63104 P.O. BOX 1172TORRES, ANDREW M LYNCHBURG COLLEGE (314) 577-8903 JERUSALEM, ISRAEL 91010DEPT OF BOTANY 1501 LAKESIDE DRIVE BITNET GERSHOM@HUJIMDUNIV OF KANSAS LYNCHBURG, VA 24501 LEONG, KINGSTONLAWRENCE, KS 66044 (804) 572-8362 DEPT OF BIOL SCIENCE CYTO GENETICS(9131 864-4257 CAL POLYTECH STATE UNIV GRANT, WILLIAM F

STONE, LARIUE E SAN LUIS OBISPO, CA n407 GENETICS LABORATORYVERO!, RICHARD DANA COLLEGE (805)546.2788 PO BOX 4000DEPT OF BIOLOGY BLAIR, NE 63008 MACDONALD CAMPUS OFNORTHLAND COLLEGE (402) 426-4101 EXT 229 LIBEY, TERRY McGILL UNIVASHLAND, WI 54806 DEPT OF HEALTH CAREERS STE ANNE DE BELLEVUE(715) 682-4531 MCI' 335 WILLIAMS, W E LANSING COMMUNITY QUEBEC, CANADA 119X 1CO

DEPT OF BIOLOGY COLLEGEWULFF, BARRY TRINITY COLLEGE 419 N CAPITOL AVE CYTOLOGYDEPT OF BIOLOGY HARTFORD, CT 06106 LANSING, MI 48901 -7211 GRANT, WILLIAM FEASTERN CONNSTATE UNIV (203) 527.3151 EXT 570 GENETICS LABORATORYWILLIMANTIC, CT 06226 OLDEN, IV/ PO BOX 4000(203) 456-2231 CHEMISTRY DEPT OF ANIMAL SCIENCE MACDONALD CAMPUS OF

IIESTON, W M OKLAHOMA STATE UNIV McGILL UNIVYOSIIIMURA, MICHAEL A CTR FOR NATURAL SCIENCES STILLWATER. OK 74078 STE ANNE DE BELLEVUEDEPT OF BIOL SCIENCE NEW YORK INSTTECII (405) 624-6670 QUEBEC, CANADA 119X 1COCAL POLYTECH STATE UNTV OLD WESTBURY, NY 11568SAN LUIS OBISPO, CA 93407 (516) 686-7665 OWEN, WILLIS L KEETE, J RICHARD(805) 546-2466 3224 N ROFF DEPT OF ANATOMY

COMPUTERS/COMP LITERACY OKLAHOMA CITY, OK 73112 CASE WESTERN RESERVECELL BIOLOGY CHARDINE, DR JOHN W (405) 271.2229 UNIV

BATES, WILLIAM K DEPT OF BIOL SCIENCES CLEVELAND, OH 44106DEPT OF BIOLOGY BROCK UNIVERSITY PARISI, ANTHONY (216) 368-2656UNIV OF NORTH CAROLINA ST CATHARINES, ONTARtO WRIGHT STATE UNIV SCHOOLGREENSBORO, NC 27412 CANADA L2S 3A1 OF MEDICINE DENTISTRY(919) 379.5391 (416) 688.5550 DAYTON, OH 45401.0927 TOWNSEND, DeWAYNE

(513) 376-6711 SCHOOL OF DENTISTRYBERNSTEIN, MAURICE H CODY, RONALD 16-262 MOOS TOWERDEPT OF ANAT & CELL BIOL DEPT OF ENVIRON & POITINGER, JAN UNIVERSITY OF MINNESOTAWAYNE STATE UNIV SCI I COMMUNITY MED RM2050 MSC MINNEAPOLIS, MN 55455

MEDICINE RUTGERS MED SCHOOL UNIV OF WISCONSIN AT (612) 625-7467540 E CANFIELD PISCATAWAY, NJ 08854 MADISONDETROIT, MI 48201 (201)463.4490 MADISON, WI 53706 DEVELOPMENTAL BIOLOGY(313) 577-1029 (608) 263-1595 HEIDCAMP, WILLIAM

CORBEIL, ROBERT R BIOLOGY DEPARTMENTCREUTZ, CHARLES DEPTS OF MATHEMATICS & SCHAAD, DOUGLAS C GUSI'AVUS ADOLPHUS COLLDEPT OF BIOLOGY COMPUT SCI DIV RES IN MED EDUC ST PETER, MN 56082UNIV OFTOLEDO UNIV OF SAN DIEGO UNIV OF WASHINGTON SC-45 (507) 931-7325TOLEDO, 011 43606 SAN DIEGO, CA 92110 SEATTLE WA 98195(419) 537-4159 (619) 2604600 EXT 4459 (206)543-9320 RA ISTON,111

DEPT OF ANATOMY S-1334GRAY. F HARRIET DUGGAN, ELEANOR L SESSLER, AMY UNIV CALIFORNIA AT SANHOLLINS COLLEGE BIOLOGY DEPT CURRICULAR SOFTWARE FRANCISCOBOX 9616 UNIV OF N CAROLINA AT STUDIO 3RD & PARNASSUS AVESHOLLINS COLLEGE, VA 24020 GREENSBORO TUFTS UNIVERSITY SAN FRANCISCO, CA 94143(703) 362-6543 ROOM 309, EBERHART BLDG MEDFORD, MA 02155 (415) 476-1861

GREENSBORO, NC 27412-5001 (617) 628.5000 EXT 2009PRESCOTT, LANSING M (919) 379-5839 STONE, LARRIE EDEPT OF BIOLOGY SKAVARIL, DR RUSSELL V DANA COLLEGEAUG USTANA COLLEGE DURY, CARL G DEPT OF GENETICS BLAIR, NE 68008SIOUX FALLS, SD 57197 OFFICE OF COMP & INFO OHIO STATE UNIV (402) 426-4101 EXT 229(605) 336-4719 SERVICES COLUMBUS, 011 43210

TEMPLE UNIV HLTH SCI CTR (614) 422-1310 VOORHEES, FRIEEIDCAMP, WILLIAM 3333 NO BROAD ST, RM 327 DEPT OF BIOLOGYBIOLOGY DEPARTMENT PHILADELPHIA, PA 19140 STRINGFELLOW, HART R CENTRAL MISSOURI STATEGUSTAVUS ADOLPHUS COIL (215) 221-3479 DEPT OF DENTAL EDUCATION UNIVST PETER, MN 5608 2 UNIV OF FLORIDA COLLEGE WARRENSBURG, MO 64093(507) 931 -7 .,25 HOFMANN, WALLACE C OF DENTISTRY (816) 429-4933

0742. 3233/88/50.00+ 2-00 0 1988 BY NATIONAL, RESOURCE FOR COMPUTERS IN LIFE SCIENCE EDUCATION


AIMS AND SCOPE The goal of Computers in Life Science Education is to provide a means of communicationamong life science educators who anticipate or are currently employing the computer as aneducational tool. The range of content includes, but is not limited to, articles focusing oncomputer applications and their underlying philosophy, reports on faculty/studentexperiences with computers in teaching environments, and software/hardware reviews inboth basic science and clinical education settings.

INVITATION TO CONTRIBUTORS Articles consistent'with the goals of Computers in Life Science Education are invited forpossible publication in the newsletter.

PREPARATION AND SUBMISSION OF MATERIAL

Articles submitted for publication should not exceed 2000 words and should be typewritten,double spaced, with wide margins. The original and two copies including two sets of figuresand tables should be sent to the Editor: Dr. Harold Modell, NRCLSE, Mail Stop RC-70,University of Washington, Seattle, WA 98195.

Title page should include full title, list of authors, academic or professional affiliations, andcomplete address and phone number of the corresponding author.

Illustrations should be submitted as original drawings in India ink or sharp, unmountedphotographs on glossy paper. The lettering should be such that it can be legible afterreduction (width of one column = 5.7 cm).

Reference style and form should follow the "number system with references alphabetized"described in the Council of Biology Editors Style Manual. References should be listed inalphabetical order by the first author's last name, numbered consecutively, and cited in thetext by these numbers.

RESPONSIBILITY AND COPYRIGHT

Authors are responsible for accuracy of statements and opinions expressed in articles. Allauthors submitting manuscripts will be sent a copyright transfer form to complete. Thecompleted form must be returned before the work will be published.

SUBSCRIPTION INFORMATION Computers in Life Science Education is published monthly by National Resource forComputers in Life Science Education, Mail Stop RC-70, University of Washington, Seattle,WA 98195. Subscription rate is S30.00 for 12 issues, including postage and handling in theUnited States and Canada. Add 520.00 for postage (airmail) in Mexico and Europe andS23.00 for the rest of the world.

This newsletter has been registered with the Copyright Clearance Center, Inc. Consent isgiven for copying of articles for personalor internal use, or for the personal or internal use ofspecific clients. This consent is given on the condition that the copier pay through the Centerthe per-copy fee stated in the code on the first page for copying beyond that permitted by theUS Copyright Law. If no code appears on an article, the author has not given broad consentto copy and permission to copy must be obtained directly from the author. This consent doesnot extend to other kinds of copying, such as for general distribution, resale, advertising andpromotional purposes, or for creating new collective works.

Address orders, changes of address, and claims for missing issues to NRCLSE, Mail StopRC-70, University of Washington, Seattle, WA 98195. Claims for missing issues can behonored only up to three months for domestic addresses and six months for foreignaddresses. Duplicate copies will not be sent to replace ones undelivered due to failure tonotify NRCLSE of change of address.

Address editorial correspondence to Harold I. Modell, PhD, NRCLSE, Mail Stop RC-70,University of Washington, Seattle, WA 98195. (BITNET MODELL@UWALOCKE)

POSTMASTER: Send address changes to Computers in Life Science Education, NRCLSE,Mail Stop RC-70, University of Washington, Seattle, WA 98195.

01988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENCE EDUCATION0742-3233/8840.00 + 2.00

I

VOLUME 5 NUMBER 3, MARCH 1988 CLSEE3 5(3)17-24, 1988 ISSN 0742-3233

I. 11111111111111111111111111111111111

HAROLD I. MODELLDepartment of RadiologyUniversity of Washingtco.Seattle, Washing=


THEODORE J. CROVELLOGraduate Studies and ResearchCalifornia State University, Les AngelesLos Angeles, California


TSEGAYE HABTEMARIAMSchool of Veterinary MedicineTuskegee UniversityTuskegee, Alabama


TERRY M. MIKITENGraduate School of Biomedical SciencesUniversity of Texas Health Science CenterSan Antcnio,Texas

JAMES E. RANDALLM:rent of Physiology

anUniversityBloomington, Indiana

PATRICIA SCHWJRIANCollege of NursingOhio State UniversityColumbus, Ohio


JAMES W. WOODSLister Hill National Caite

for Biomedical CommumcionsNational Library of MedicineBethesda, Maryland

DOROTHY WOOLEY-McKAYDepartment of BiologyGlendale Community CollegeGlendale, Arizona

GLEN YOSHIDADepartment of Life SciencesLos Angeles Southwest CollegeLes Angeles, California

NRCLSE

CONTENTS

CLSE 1987-88 COLLEAGUE DIRECTORY PART II

KEEPING ABREAST OF THE LITERATURE

17

23

CLSE 1987-88 COLLEAGUEDIRECTORY - PART IIThe primary goal of the National Re-source for Computers in Life ScienceEducation ( NRCLSE) is to cultivatecollaborative efforts among life sciencefaculty interested in using the computeras a teaching tool.

listing that follows is a continua-tiL.. of the 1987-88 directory publishedlast month. It is updated from the1986-87 directory and was drawn pri-marily from respondents to question-naires printed in CLSE. It is intendedto help readers identify colleagues withcommon interest areas.

The listings are arranged by the con-tent areas identified in response to thequestion, "What content areas do youteach?" As a result, entries may appear

ECOLOGYBEAVER, JAMES SDEPT OF AGRONOMY ANDSOILS

COLLEGE OF AGRIC SCIUNIV OF PUERTO RICOMAYAGUEZ, P R 00708(809) 834.4040 EXT 2380

under more than one heading.Although every attempt has been

made to ensure that the information iscurrent and correct, it is likely thatsome errors appear in this list. Weapologize in advance for any inconve-niences that may arise due to suchoversights. The directory will becompleted in next month's issue.

If you are aware of other colleaguesthat should be listed, please send thepertinent information (name, address,phone number, BITNET address,teaching content area) to NRCLSE,Mail Stop RC-70, Univ. of Washing-ton, Seattle, WA 98195 or let us knowvia BITNET. NRCLSE's BITNETaddress is MODELL@UWALOCICE.

.BELLMER, SISTER ELIZA-BETH

TRINITY COLLEGEWASHINGTON, DC 20017(202) 939-5190

BOWKER, LESLIE SDEFT OF BIOL SCIENCE

CAL POLYTECH STATEUNIV

SAN LUIS OBISPO, CA93407

(805) 546-2788

BOYNTON, DR JOHN EST JOSEPH'S COLLEGE

0742-3233/88/SO.00 + 2.00 ©1988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENCE EDUCATION

18 COMPUTERS IN LIFE SCIENCE EDUCATION, VOLUME 5, NUMBER 3, MARCH 1988

155 ROE BLVD GILES, ROBERT Hr UNIVERSITY OF PORTLAND DEPT OF BIOLOGYPATCHOGUE, NY 11772 DEPT OF FISHERIES & 500 N WILLAMETTE BLVD SAN JOSE STATE UNIV(516) 654-3200 WILDLIFE SCIENCE PORTLAND, OR 97203 SAN JOSE CA 95192

VIRGINIA POLYTECH INST & (503) 283-7175 (408) 277-2355CACCAMISE, DONALD F STATE UNIVDEPT OF ENTOMOLOGY BLACKSBURG, VA 24061 STANTON, GEORGE E EMBRYOLOGYRUTGERS UNIV (703) 961-5910 DEPT OF BIOLOGY BELLMER, SISTER ELIZA-NEW BRUNSWICK. NJ 08903 COLUMBUS COLLEGE BETH H(201)932.9459 HILTON, DONALD FT COLUMBUS, GA 31993 TRINITY COLLEGE

DEPT OF BIOL SCIENCE (404) 568-2065 WASHINGTON. DC 20017CALDWELL, DOUG BISHOP'S UNIV (202)939.5190DEPT OF APPLIED MICRO & LENNOXVILLE, QUEBEC UNGAR, IRWIN A

FOOD SCI CANADA J1 M 127 DEPT OF BOTANY BEPNSTEIN, MAURICE HUNIV OF SASKATCHEWAN (819) 569.9551 OHIO UNIV DEPT OF ANAT & CELL BIOLSASKATOON, SK S7N OWO ATHENS, OH 45701 WAYNE STATE UNIV SCHCANADA HOLLAND, V L MEDICINE(306) 966-5026 DEPT OF BIOL SCIENCE VAN AMBURG, GERALD L 540 E CANFIELD

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UNIV OF WISC AT OSHKOSH BOX 19389GAMES. ROGER OSHKOSH, WI 54901 ARLINGTON, TX 76019 RAWITCH ALLEN BDEPT OF BIOL SCIENCE (414) 424-3068 (817) 273-2129 DEPT OF BIOCHEMISTRYCAL POLYTEGI STATE UNIV UNIV KANSAS MED CTRSAN LUIS OBISPO, CA 93407 SNOW, MICHAEL D ELECTRON MICROSCOPY KANSAS CITY, KS 66103(805) 546-2551 DEPT OF PHYS & LIFE SCI SAVAGE, WAYNE (913)588.6957

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SAGE, MARTIN THIEL COLLEGE (319) 273-2670 CARICO, JAMES EDEPT OF BIOLOGY GREENVILE, PA 16:25 DEPT OF BIOLOGYUNIV OF MISSOURI AT ST (412) 588-7700 STANSFIELD, WILLIAM LYNCHBURG COLLEGELOUIS DEPT OF BIOL SCIENCE LYNCHBURG, VA 24501ST LOUIS, MO 63121 PICKERLNG, JOHN CAL POLYTECH STATE UNIV (804) 522-8366(314) 553-6218 DEPT OF ENTOMOLOGY SAN LUIS OBISPO, CA 934 07UNIV OF GEORGIA (805) 546-2875 CORBEIL, ROBERT RSCHMIDT, THOMAS J ATHENS, GA DEPT OF MATH & COMPUT SCIDEPT OF PHYSIOL & BIOPHYS (404) 542-2816 STANTON, GEORGE E UNIV OF SAN DIEGOUNIV OF IOWA DEPT OF BIOLOGY SAN DIEGO, CA 921105-432 BOWEN SCIENCE BLDG WILLIAMS. STANLEY C COLUMBUS COLLEGE (619) 260-4600 EXT 4459IOWA CITY, IA 522042 DEPT OF BIOL SCIENCES COLUMBUS, GA 31993

(319) 353-4018 SAN FRANCISCO STATE UNIV (404) 568-2065 DAVIS, THOMAS MSAN FRANCISCO, CA 94132 DEPT OF PLANT SCIENCE/WOLF, RICHARD C WILLIAMS, W E GENETICSDEPT OF PHYSIOLOGY ENVIRONMENTAL SCIENCE DEPT OF BIOLOGY UNIV OF NEW HAMPSHIREUNIV OF WISCONSIN FORTN'ER, ROSANNE TRINITY COLLEGE DURHAM, NH 03824-3597MADISON, WI 53706 OHIO STATE UNIV HARTFORD, CT 06106 (603) 862.3217(608) 262-2939 SCHOOL OF NAT RESOURCES (203) 527-3151 EXT.570COLUMBUS, OH 43210 DICKINSON, WINIFREDZIMMERMAN, JAY (614)4=-2265 FAMILY PRACTICE DEPT OF BIOLOGYDEPT OF BIOL SCIENCES

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(501) 575.2451 SEAGER, BOB DEPT OF BIOLOGY LA BAR, MARTINDEPT OF BIOLOGY MONTANA STATE UNIV CENTRAL WESLEYANODE, PHILIP UNIV OF NORTHERN IOWA BOZEMAN, MT 59717 COLLEGEDEPT OF BIOLOGY CEDAR FALLS, IA 50614 (406) 994-2670 CENTRAL, SC 29630

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(803) 639-2453 WASHBURN UNIV OFTOPEKA KESSLER, SISTER IRMA SPITZNAGEL, DR JOHN K1700 COLLEGE AVE DEPT OF BIOLOGY DEPT OF MICROBIOLOGY &

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CASE WESTERN RESERVE U MICKUS, JOHNSEAGER, BOB CLEVELAND, OH 44106 DEPT OF BIOLOGY VOORHEES, FRANK RAYDEPT OF BIOLOGY (216) 368.2656 ILLINOIS BENEDICTINE COLL DEPT OF BIOLOGYUNIV OF NORTHERN IOWA LISLE, 1. 60532 -0900 CENTRAL MISSOURI STATECEDAR FALLS, IA 50614 HISTOLOGY (312) 960 -1500 EXT 509 UNIV, WCM 306(319) 273-2670 ADAMS, DON WARRENSBURG, MO 64093

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SCHOOL OF NATURALRESOURCES

2021 COFFEY ROADCOLUMBUS, 011 43210(614) 422-2265

DEPT OF BIOLOGYGEORGIA SOUTHWESTERNCOLLEGE

AMERICUS, GA 31709(912) 928-1250

SCHADLER, DANIEL LDEPT OF BIOLOGYOGLETHORPE UNIV4484 PEACHTREE ROAD NEATLANTA, GA 30319.2797

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CAL POLYTECH STATE UNIVSA UM OBISPO, CA 93407

DEPT OF BIOLOGYEMPORIA STATE UNIV

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DEPT OF MED TECHNOLOGYGEORGIA STATE UNIV

DEPT OF BIOLOGYUNIV OF MICHIGANFRAMINGHAM, MA 01701

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BRESCIA COLLEGEDEPT OF MICROBIOLOGYUCLA

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CEDAR FALLS, IA 50614 3333 GREEN BAY RDEISENSTARK, A PHILIPPI, WV 26416 (319)273.2723 NORTH CHICAGO, IL 60064DEPT OF BIOL SCIENCE (304) 457.1700 EXT 243(312) 578-3000 EXT 458UNIV OF MISSOURI

BOND, CLIFFORDCOLUMBIA, MO 65211 SABOSKI, ELEANOR M DEPT OF MICROBIOLOGYNEUROBIOLOGY/NEUROS-(314) 882.7098 NEW ENGLAND COLLEGE MONTANA STATE UNIV CIENCEHENNIKER, NH 03242 BOZEMAN, MT 59717 BUCHHOLZ, ROBERT IIEIDER, DR BETTY (603) 428-2374 (406)994.4130 DEFT OF BIOLOGY

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NEAFSEY, E JDEPT OF ANATOMY

HODSON, KAY ESCHOOL OF NURSING CN355BALL STATE UNIVMUNCIE, IN 47306(317) 285-5583

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COLLEGE OF DENTISTRYUNIV OF IOWAIOWA CITY, IA 52242(319) 353-7370

ORNITHOLOGYLOYOLA UNIV MED CTR KRISTENSEN, KAREN NUTRITION CYR, ANDREMAYWOOD, IL 60153 TRI-COLLEGE UNIV OF NURS DICKINSON, WINIFRED DEPT OF BIOLOGY(312) 531-3355 NORTH DAKOTA STATE UNIV DEPT OF BIOLOGY UNIV OF SIIERBROOKE

FARGO, ND 58105 UNIV OF STEUBENVILLE SHERBROOKE, QUEBECRALSTON, H J (701) 237-8170 STEUBENVILLE, OH 43952 CANADA JIK 2R1DEPT OF ANATOMY S-1334 (614) 283-3771 (819) 821-7074UNIV CA SAN FRANCISCO KAREN, KATHERINESAN FRANCISCO, CA 94143 COLLEGE OF NURSING HARROLD, ROBERT PARRISH, DR JOHN(415) 476.1861 SETON HALL UNIV DEPT OF ANIM & RANGE SCI DEPT OF BIOLOGY

SOUTH ORANGE, NJ 07079 NORTH DAKOTA STATE UNIV EMPORIA STATE UNIVSTOUT, JOHN (201) 761-9303 FARGO, ND 58105 EMPORIA, KS 66801DEPT OF BIOLOGY (701) 237-7659 (316) 343-1200ANDREWS UNIV LARSON, DONNABERRIEN SPRINGS, MI 49104 KIRKOF SCHOOL OF NURSING HESTON. W M VERCH, RICHARD(616) 471-3243 GRAND VALLEY STATE COLL CTR FOR NATURAL SCIENCES DEPT OF BIOLOGY

ALLENDALE, MI 49401 NEW YORK INST TECH NORTHLAND COLLEGEYORK, DR DONALD H (616)895.3540 OLD WESTBURY, NY 11568 ASHLAND, WI 54806DEPT OF PHYSIOLOGY (516) 686-7665 (715) 682-4531 EXT 335UNIV OF MISSOURI LIBEY, TERRYMA415 MEDICAL SCI BLDG DEPT OF HEALTH CAREERS HU, CUING YUAN PARASITOLOGYCOLUMBIA, MO 65212 LANSING COMMUNITY DEPT OF ANIMAL SCIENCE BAUR, JOHN M(314)882-7168 COLLEGE OREGON STATE UNIV DEPT OF BIOLOGY

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ABBATE, JUDITH (502) 685.3131 EXT 276COLLEGE OF NURSING LOUIS, MARGARET OLTJEN, J WUNIV OF RHODE ISLAND DEPT OF NURSING DEPT OF ANIMAL SCIENCE HILTON, DONALD FJKINGSTON, RI 02881 UNIV OF NEVADA, LAS VEGAS OKLAHOMA STATE UNIV DEPT OF BIOL SCIENCES(401) 792-2766 LAS VEGAS, NV 89154 STILLWATER, OK 74078 BISHOPS UNIV

(405)624.6670 LENNOXVILLE QUE JIM 127BENEDICT, DR SUSAN MIKAN, KATHLEEN CANADA209 MOUNTAIN RIDGE DRIVE SCHOOL OF NURSING SCHNEIWEISS, JEANNETTE W (819) 569.9551HUNTSVILLE, AL 35801 UNIV ALABAMA AT DEPT OF BIOLOGY(205) 536-7101 BIRMINGHAM IIOFSTRA UNIV PATHOLOGY

BIRMINGHAM, AL 35294 HEMPSTEAD, NY 11550 FIEBACK, DANIELBUDIN, WENDY (205) 934-4837 (516) 560-5521 DEPT OF ANAT SCIENCECOLLEGE NURSING UNIV OF OKLAHOMA HEALTHSETON HAIL UNIV MURRAY. LAURIE R SHORT, SARAH 11 SCIENCE CENTERSOUTH ORANGE, NJ 07079 COLLEGE OF NURSING SYRACUSE UNIV PO BOX 26901 BMSB540(201) 761-9299 UNIV OF RHODE ISLAND 112 SL OCUM HALL OKLAHOMA CITY, OK 73190

KINGSTON, RI 02881.0814 SYRACUSE, NY 13244.1250 (405) 271-2377CLAUS, SACHIKO K (401) 792-2766 (315) 423.2386DEPT OF NURSING GANDER, G WMSAGINAW VALLEY STATE RONALD, DR JUDITH WAGNER, MARTIN J DEPT OF PATHOLOGY

COLLEGE SCHOOL OF NURSING DEPT OF BIOCHEMISTRY MEDICAL COLL OF VIRGINIAUNIVERSITY CTR, MI 48710 SUNY AT BUFFALO BAYLOR COLLEGE OF BOX 662(517) 790.4129 BUFFALO, NY 14214 DENTISTRY RICHMOND, VA 23298

(716) 831-3045 DALLAS, pc 75246 (804) 786.7719DREHER, MARY ANN (214) 828.826125034 SHEFFIELD ROAD SCHARF. MARY ANN JOTIIY, DR SERGEGLEN ELLYN, IL 60137 COLLEGE OF NURSING ORAL HISTOLOGY DEPT OF PATHOLOGY

SETON HALL UNIV FEEBAOC, DANIEL McGILL UNIVGARDINER, MARY SOUTH ORANGE, NJ 07079 DEPT OF ANAT SCIENCE 3775 UNIVERSITY STRED DEER COLLEGE (201) 761.9293 UNIV OF OKLAHOMA HEALTH MONTREAL, QUEBEC 113A 2134RED DEER, ALBERTA T5N 5115 SCIENCE CENTER CANADACANADA SZNCLAIR, VAUGHN G PO BOX 26901 BMSB540 (514) 842-1231(403) 342-3362 VANDERBILT UNIV SCHOOL OKLAHOMA CITY, OK 73190

OF NURSING (405) 271-2377 KLAAS, JUDY CGROBE, S J NASI1VILLE. TN 37235 DEPT OF BIOLOGYSCHOOL OF NURSING (615) 322-2813 ORAL PATHOLOGY FRAMINGHAM STATE COLLUNIV OF TEXAS ABBEY, LOUIS M BOX 2000AUSTIN, pc 78701 SKIBA, DIANE DEPT OF ORAL PATHOLOGY FRAMINGHAM, MA 01701(512) 471-7311 23 HOLMES ST VIRGINIA COMMONWEALTH (617) 620-1220 EXT 459

DEDHAM, MA 02026 UNIV[LASKIN, DOUGLAS E (617)461.0276 BOX 566 MCV STATION LEIG1riON, FREDERICK ASCHOOL OF NURSING RICHMOND, VA 23298 DEPT OF VET PATHOLOGYUNIV TEXAS MEDICAL THERE, LINDA Q (804) 786-0547 UNIV OF SASKATCHEWANBRANCH R-49 36203 DERBY DOWNS SASKATOON, SK S7N OWO

GALVESTON,TX 77550 SOLON, OH 44139 FINKELSTEIN, MICHAEL CANADA(409) 761-1577 (216) 248-3537 DEPT OF ORAL PATHOLOGY (306) 966-7293

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COMPUTERS IN LIFE SCIENCE EDUCATION, VOLUME 5, NUMBER 3, MARCH 1988 23

MASSEY, K LORNE OF MEDICINE PHARMACOLOGY I IAGEN, STANLEY VONDEPT OF PATHOLOGY 540 E CANFIELD BEHEI, MARGARET A DEPT OF PHARMACOLOGYCOLLEGE OF MEDICINE DETROIT, MI 48201 ROUTE 3 BOX 77 NEW JERSEY MEDICALUNIV OF SASKATCHEWAN (313) 577-5152 PI III. CAMPBELL, AL 35585 scnooi.SASICATOON, SAS K S7N OWO(205) 993.5331 NEWARK. NJ 07103CANADA YOSIIIMURA, MICHAEL A (201)456-4335(306) 966-2167 DEPT OF BIOLOGICAL CONAHAN, SHAWN T

SCIENCE DEPT OF PI IYSIOIAGY SPECIIT, PHILIP CMcMANNO, BRUCE CAL POLYTECH STATE LOUISIANA STATE UNIV MED PHARMACOLOGY DEP.DEPT OF PATHOLOGY UNIVERSITY CENTER UPR MEDICAL SCHOOLUNIV NEBRASKA MEDICAL SAN LUIS OBISPO, CA 93407 1901 PERDIDO ST GPO BOX 5067CENTER (805) 546-2466 NEW ORLEANS, LA 70112 SAN JUAN, PR 0093642ND & DEWEY AVE(504)582-5783 (809) 758-2525 EXT 1300OMAHA, NE 68105 PEDIATRICS

(402) 559-5192 WINTER,ROBERTI GWINN, DR JOHN F SPYKER, D ACHILDREN'S MEMORIAL HOSP DEPT OF BIOLOGY UNIV OF VIRGINIA SCHOOLPALMER, KENNETH C 2300 CHILDREN'S PLAZA UNIV OF AKRON OF MEDICINEDEPT OF PATHOLOGY CHICAGO, IL 60614 AKRON. 01144325 BOX 484 MEDICAL CENTERWAYNE STATE UNIV SCHOOL (312) 880-4196 (216) 375-7160 CI IARLOITESVILLE, VA 22908


The following citations are presentedas part of a quarterly feature in CLSEdesigned to help readers become awareof current literature pertinent to com-puter applications in life scienceeducation.

Beyon RJ: Computers in biochemicaleducation: MBT-NET, the Liver-pool biochemistry/microbiologyteaching network. Biochem SocTrans 15(3149-351, 1987.

Boulet M: Educational software designusing a diagnostic approach.Computers and Education11(3):219-227, 1987.

Clark D: Aspects of menu design. JComp Assisted Learning 2(3):172-177, 1986.

Dc Bloois M: Anticipating compactdisc-interactive (CD-I): Ten guide-lines for prospective authors. EducTech 27(4):25-27, 1987.

Douglass SE: Computers and thecopyright law. EducationalHorizons 66(1):46-48, 1987.

Gantt TJ et al: A comparison ofcomputer-assisted instruction andtutorials in hematology and oncol-ogy. J Med Educ 62(11):918-922,1987.

Gwinnett A: Computers in trainingnurses? Br J Healthc Comput3(6):21, 23, 25, 1986.

Hosie P: Adopting interactive video-disc technology for education. EducTech 27(7):5-10, 1987.

Johnston R et al: Computers inrespiratory therapy. AARC Times11(5):18, 1987.

Kunstaetter R: Intelligent physiologicmodelling: an application of know-ledge based systems technology tomedical education. ComputMethods Programs Biomed 24(3):213 - 225,1987.

Lay C et al: The legal implications ofrepurposing videodiscs. VideodiscMonitor 5(7):23, 1987.

Miller K et al: Audio-enhancedcomputer-assisted learning inscience teaching. Biochem SocTrans 15(3):351-354, 1987.

Moshinskie J: Terminally trained...computer-assisted instruction.Emergency 19(8):42-43, 58-59,1987.

Park 0 et al: Conventional CBI versusintelligent CAT: suggestions for thedevelopment of future systems.Educ Tech 27(5):15-21, 1987.

Rambally GK: The AI approach toCAL Computing Teacher 13(7):39-42, 1986.

Rambally GK et al: Human factors inCAI design. Comput Educ11(2):149-1.53, 1987.

Raymond J: The computedzed over-head projector. Computers andEducation 11(3):181-195, 1987.

Sandoval VA et al: A comparison offour simulation and instructionalmethods for endodontic review. JDent Educ 51(9):532-538, 1987.

Schwirian PM: Evaluation research incomputer-based instruction. Dilem-mas and directions. Comput Nurs5(4):128-131, 1987.

Strack G: The computer takes a fieldtrip. Comput Teach 13(7):12-17,1986.

Suzuki K: A short-cycle approach toCAI development: three stageauthoring for practitioners. EducTech 27(7):19-24, 1987.

Tennyson RD: MAIS: An educationalalternative of ICAI. Educ Tech27(5):22-28, 1987.

Thomson I et al: Interactive videodiscsin the life sciences: the use of'generic' discs in the teaching of cellbiology. Biochem Soc Trans15(3):354-356, 1987.

Troutner J: Computer literacy. Prob-lem solving software for scienceclasses. J Comp Math Sci Teach6(3):9-11, 1987.

0742-3233/88130.00 + 2.00 0 1988 BY NATIONAL RESOURCE FOR COMPUTERSIN LIFE SCIENCE EDUCATION

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AIMS AND SCOPE The goal of Computers in Life Science Education is to provide a means of communicationamong life science educators who anticipate or are currently employing the computer as aneducational tool. The range c f content includes, but is not limited to, articles focusing oncomputer t.pplications and their underlying philosophy, reports on faculty/studentexperiences with computers in teaching environments, and software/hardware reviews in

both basic science and clinical education settings.

INVITATION TO CONTRIBUTORS Articles consistent with the goals of Computers in Life Science Education are invited forpossible publican= in the newsletter.

PREPARATION AND S UriMISSION OF MATERIAL







SUBSCRIPTION INFORMATION Computers in Life Science Education is published monthly by National Resource forComputers in Life Science Education, Mail Stop RC-70, University of Washington, Seattle,WA 98195. Subscription rate is $30.00 for 12 issues, including postage and handling in theUnited States and Canada. Add $20.00 for postage (airmail) in Mexico and Europeand$23.00 for the rest of the world.

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VOLUME S NUMBER 4, APRIL 1988

HAROLD I. MODELLDepartment of RadiologyUniversity of WashingtonSaul; Washington

MARCEL BLANCHAERDepanmen: of BiochemistryFaculty of MedicineUniversity of ManitobaWinnipeg. Manitoba

THEODORE J. CROVELLOGraduate Studies and ResearchCalifornia State University. Los AngelesLos Angela:, California

JAMES W. ECKBLADDepartment of BiologyLutlma CollegeDecorah. Iowa

iSEGAYE HABTEMARIAMSchool of Veterinary Medicine'Pinkeye UniversityTuskegce, Alabama


TERRY M. MBUTENGraduate School of Biomedical SciencesUniversity of Texas Health Science CanerSan Antonio, Texas


PA1RICIA SCHWIRIANCollege of NursingOhio State UniversityColumbus, Ohio

RICHARD STULLCollege of PharmacyUniversity of ArkansasLittle Rode, Manus

JAMES W. WOODSLister l till National Centerfor Biomedical Communications


DOROTHY WOOLEY-McKAYDepartment of BiologyGlenda': Community CollegeGlendale, Arizona


NRCLSE

CLSEE3 5(4)25-32, 1988 ISSN 0742-3233

111111111111111111111

CONTENTS

APPLE He MEDIATED WORKSTATIONS IN THEUNDERGRADUATE PHYSIOLOGY

Michael A. Kolitsky25

CLSE 1987-88 COLLEAGUE DIRECTOP.Y - PART III 29

APPLE //e MEDIATEDWORKSTATIONS IN THEUNDERGRADUATEPHYSIOLOGY LABORATORYMichael A. KolitskyDepartment of Biological Sciences, California Lutheran University,Thousand Oaks, California

Several years ago, the National ScienceFoundation established the College Sci-ence Instrumentation Program (CSIP)to upgrade laboratory instrumentationin undergraduate science education. Iwas the principle investigator for aCSIP award in 1985 (IICSI-8557398)that permitted me to assemble fiveApple //e-mediated workstations foruse in my physiology laboratories. Theworkstation concept exploited the ideathat a microcomputer, when linked toan appropriate environmentally sen-sitive interface, could be transformedinto a versatile, data-gathering labora-tory instrument.2.3 I developed soft-ware to permit use of the Apple //c

monitor screen as a strip chart recorderand, when the desired screen imagewas obtained, students could employ adot-matrix printer to make hard copiesof the graphics screen for their labnotebooks. By simply changing theinterface or the controlling software, asingle workstation could be made to actlike many single-function laboratoryinstruments. For example, my studentshave used an inexpensive, self-con-structed photosensitive interface totransform the Apple //e into a transmis-sion densitometer to study red bloodcell hemolysis,' a kymograph to inves-tigate frog skeletal muscle contraction?a pncumograph to investigate %uman

0742-3233/88/S0.00 + 2.00 0 1988 BY NATIONAL RESCJRCE FOR COMPUTERS IN LIFE SCIENCE EDUCATION

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26 COMPUTERS IN LIFE SCIENCE EDUCATION, VOLUME 5, NUMBER 4, APRIL 1988

respiratory mechanics and ac. a deviceto measure human reaction time inresponse to audio or visual stimuli.Several types of commercially avail-able interfaces were also employed toallow the Apple //e to act as a pH meter(HRM Software, 175 Tompkins Ave,Pleasantville, New York 10570) and adigital oscilloscope (W.H. Nail Co,1096 C oro Dam Blvd., Oroville, CA95965) for electrophysiological studies.For an excellent description of the useof the Apple as a digital storage oscil-loscope, I refer you to a report by MaryCoyne.'

In this report, I would like to focus onthe photosensitive interface and its ap-plication in the undergraduate physio-logy laboratory. The simplicity of thephotosensitive interface may be seen inthe diagram of the schematics shown inFigure 1. An FPT-100 phototransistoris used to measure light intensity bychanges in resistance. A 16 pin DualInput Plug (DIP) is used to connect thecollector pin and the emittor pin of thephototransistor to the +5 Volt andground pins of the Apple game I/Oconnector. The DIP and phototransis-tor plus the "breadboard" (microcircuitboard) which hold the components costunder $20.00 to construct and car beobtained from local or regional elec-tronics suppliers.

SOFTWARE DESIGN

Applesoft BASIC was used to write thedata sampling and display programs for

FIGURE 1. Schematic for photosensitiveinterface. A view of the underside of anFPT -100 phototransistor (upper right)showing placement of the pins and theirconnection to the DIP connector. E=emitterpin, B=base pin, C=collector pin. Pin 1 ofthe DIP is the +5 volt source, and pin 6 isthe ground (GC0) connection.

all expenments in this report. Theseprograms cause the monitor screen toact like a strip chart recorder by draw-ing a single trace across the high reso-lution graphics screen from left to right.In order to do this in BASIC, the state-ment Y = PDL(0) was used to samplethe interface resistance reading andreturn a value ranging from 0 to 255.The value for Y determines the pixel tobe lit on the vertical axis of the graph-ics screen. The movement of the tracefrom left to right is driven by FOR-NEXT loop statements that increase thevalue of X by one each time the loop isexecuted. When the statement, HPLOTX,Y, is included within the FOR-NEXT loop, pixels are lit in sequencefrom left to right to simulate a stripchart recording.

A Grappler card in expansion slot 4connects to a dot-matrix printer andgives students the option to print thehigh resolution graphics screen in thedesired format. Selected screens mayalso be saved in floppy disk files forreview at a later time.

STUDENT APPLICATIONSStudents used the photosensitive inter-face as a transmission densitometer fortwo types of experiments with redblood cells (RBCs). First, slow hemo-lysis kinetics of RBCs were followedafter introduction of the RBCs intosolutions of 0.5 M urea, glycerol orglucose.3 In the second type of experi-ment, students examined the effects oftonicity on RBC shape and size. Forboth experiments, a 3 Volt miniaturelamp was positioned so that its lighttraveled through a test tube containingthe RBC suspension before falling onthe phototransistor. The results from atypical slow hemolysis experiment inwhich an RBC suspension was intro-duced into a solution of 0.5M glycerolcan be seen in Figure 2. When RBCswere added, the trace immediatelymoved upward indicating less light fal-ling on the phototransistor due to an in-crease in light scattered by the RBCs.Thereafter, the slow downward tracemovement to the baseline gave a mea-sure of hemolysis time and indicatedthat more light reached the phototran-sistor as light scattering decreased dueto RBC swelling and eventual hemoly-

116f: HEMOLY SISIN

R.N.-GLYCEROL

ABC ADO IT ION1 30 seconds

FIGURE 2. Red blood cell slow hemolysiskinetics following addition to 0.5M glyc-erol. A I)igi -View digitizing system (New-tek, Inc., 701 Jackson Suite B3, Topeka KS66603) was used to transfer the originalresults from the Apple //e monitor to anAmiga 1000 computer serving as a low costgraphics workstation to permit addition ofexplanatory text using an Electronic ArtsDeluxe Paint program. A photograph wasthen made using a Polaroid Palette system.All other figures and the single table wereproduced using she Amiga graphics work-station.

sis. Hemolysis time under these condi-tions also indicates how fast moleculespass through the RBC membrane. Stu-dents find that urea solutions hemolyzeRBCs in under 5 seconds, glycerol so-lutions exhibit hemolysis times around30 seconds, and in glucose solutions,hemolysis is still incomplete even after90 seconds. Students may concludethat there is a relationship betweenmolecular size and the ability to passthrough a membrane.

The results of tonicity studies inwhich RBCs were suspended in differ-ent NaCI concentrations are shown inTable 1. Transmitted light decreases asthe salt concentration increases to 2%.By observing RBC morphology with

Table 1. Use of photosensitive interface tomeasure light transmitted through RBCssuspended in different salt concentrations.Higher interface readings indicate morelight falling on the phototransistor.

TRANSM I S SION DENS I TOMETRY

LIGHT TRANSMITTED BY ROC'SIN DIFFERENT CONCENTRATIONSOF SODIUM CHLORIDESOLUTION INTERFACE READING

DISTILLED H2O 237

0.431 NaCl 213

0.851 NaCl 177

2.8Y. Had 128


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COMPUTERS IN LIFE SCIENCE EDUCATION, VOLUME 5, NUMBER 4, APRIL 1988 27

the light microscope, students can alsorelate RBC shape with its ability totransmit and scatter light.

The photosensitive interface was alsoutilized as an updated version of a ky-mograph to study frog muscle contrac-tion. A typical kymograph arrange-ment for muscle study was employedexcept that a phototransistor hung fromthe tip of the muscle lever instead of anink writing stylus, and an Apple //emonitor replaced the revolving drum.The phototransistor extended into ashort length of PVP (polyvinylpro-pylene) pipe and was illuminated by a 3Volt miniature lamp positioned at thebottom. During contraction, the musclelifted the lever and phototransistoraway from the light source, and whenthe muscle relaxed, the phototransistorreturnd to its original position. Thedetected change in light intensity wasdisplayed on the Apple //e monito:screen. The results in Figure 3 wereobtained by measuring individual mus-cle contraction-relaxation responses toa series of electrical stimuli of increas-ing voltage. The software was de-signed to wait for the student team toget everything ready for stimulation.When ready, the team signals the pro-gram to enter a 15 second period duringwhich they stimulate the muscle withthe selected voltage. If the muscle con-tracts, the monitor screen displays thedetected light change as a single, verti-cal line above the baseline. At the endof 15 seconds, the program moves thebaseline to the right a short distanceand waits again for the student team toprepare the muscle for the next voltagestimulus.

The strip chart recorder mode wasused to study muscle contraction byincreasing the frequency of stimulationwith supramaximal threshold voltage.Selected results are shown in Figure 4.As with the traditional kymograph, in-dividual muscle contraction-relaxationcycles are observed at low frequenciesof stimulation (Figure 4A), and com-plete tetanus is attained at higher stim-ulation frequencies (Figure 4.4

The strip chart recorder mode soft-ware may also be used with a Mareytambour for pneumographic study ofhuman breathing mechanics. Again, atypical kymographic setup was em-

.1111.5 1.8 12 It 25 4.1 i I

IA It 1.5 2.1 2.1 5.1111.15

HINT1-11114-HAXII-111H THRESHOLD

FIGURE 3. Response of frog gastrocne-mius muscle to increasing voltage stimuli asmeasured by the photosensitive interfacewhen used with a kymograph setup.

ployed in which a bellows pneumo-graph was strapped around the sub-ject's chest wall and attached to theMarey tambour. The major differencewas that measurements were beingmade by a phototransistor hung fromthe tip of the lever resting on the.tam-bour diaphragm. Typical experimentalresults may be seen in Figure 5. Theupper trace presents a resting breathingrate and is followed by the middle tracewhich was made while the subject heldhis breath for over a minute. The low-est trace demonstrates the expected in-crease in the breathing rate and depthof inspiration that follows a prolongedbreath hold. The response of humanbreathing mechanics under a variety of

2 STIMULI PER SECOND

CC)

conditions such as hyperventilation,rebreathing of exhaled air, or duringspeech may Plco be obtained for a morecomplete stay of the physiologicalconditions governing rate and depth ofbreathing.

The photosensitive interface may alsobe utilized to measure human reactiontime to audio or visual stimuli. As avisual stimulus, the subject is asked torespond to the disappearance of asquare drawn on the graphics screen bypressing a switch that turns on a smalllight near the photosensitive transistor.The software measures the time fromthe disappearance of the square to thedetection of light by the phototransis-tor. For the audio cue, the subject isasked to respond at the time a computergenerated tone stops. The time beforethe square disappears or the tone.stopsis variable and is dependent on a valueobtained from a random number gener-ator. Reaction time may be measuredfor both the hand and the foot and fordifferences observed in response toeither audio or visual cues.

TIME AND DISTANCECONSIDERATIONSThe accuracy of time measurementusing FOR-NEXT loops must be con-sidered when using the game I/O as theentry site for signals from the photo-sensitive interface. I utilized a self-built real time clock interface connect-

C8>

II

i

4 STIMULI PER SECOND

8 STIMULI PER SECOND 15 STIMULI PER SECOND

FIGURE 4. Four separate screens (A...13) demonstrating the response of frog gastrocnemiusmuscle to supramaximal voltage stimuli presented at different frequencies. Strip chartrecorder mode software used with the photosensitive interface in the kymograph setup.

0742-3233/88/SO.00 + 100 ©1988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENCEEDUCATION

2a


ONA/V4WAAAWNORMAL. BREATHING

A,t---

(14 BREATHS PER MINUTE)

BREATH HELD

;;:.

tBREATH RELEASED(24 BREATHS PER MINUTE)

HUMAN RESPIRATORY MOVEMENTS

FIGURE 5. Photosensitive interface usedas a pneumograph to study human respira-tory movements. Strip chart recorder modesoftware modified to permit the sequentialrecording of three traces on one screen.

ed to one of the Apple expansion slotsto measure, within 0.1 second, the timethe trace took to traverse the screen atdifferent light intensities. The results,shown in Figure 6, indicate a linear re-lationship between trace time and lightintensity. At the two extremes of satu-rating light and total darkness, there isabout a 7% difference in trace time in-dicating that the FOR-NEXT loop tim-ing method collects data in a near timerather than real time mode. For manyexperiments at the undergraduate level,near time data collection may be ac-ceptable if it does not compromise stu-dent interpretation of results (eg, mus-cle contraction data in Figures 3 and 4).However, when real time data collec-tion is essential, an external clock inter-face must be utilized. For a more de-tailed discussion of analog-digital inter-faces and timing considerations, see thearticle written by Richard Olivo.4

For those experiments in which thephototransistor is moved away from thelight source, the collected data are notan accurate estimation of the actual dis-tance of the phototransistor from thelight source. The error exists because

no correction is made to account for thephysical fact that light intensity de-creases with the square of the distancefrom the light source. This is reflectedin the results shown in Figure 7 inwhich light intensity measured via thephototransistor is plotted as a functionof the phototransistor distance from thelight source. The problem can be cor-rected, however, by referring to a stan-dard curve as shown in Figure 7 totransform the intensity reading into atrue distance measurement.

CONCLUSIONThe CSIP award supported the assem-bly of five workstations. Each stationserves from three to four students andincreases greatly student access to in-strumentation. The cost per station wasconsiderably less than the funds thatwould be needed to purchase each ofthe single function instruments sepa-rately. The multifunctional nature ofthe Apple //e-mediated workstationcoupled with its lower cost when com-pared to traditional instrumentation of-

16

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M 88

68a

4m 49

29

18.6

C1,

ay.

10.8 11.8 11.2TRACE TIME (SECONDS)

11.4

FIGURE 6. Comparison of time for thetrace to move from left to right across themonitor screen at different light intensities.Trace time was meast.red accurately to 0.1second with a self-built clock interface con-nected to one of the Apple expansion slots.

DISTANCE FROM LIGHT1

FROM (CM)4

FIGURE 7. Comparison of light intensityreading from the interface with distance ofthe phototransistor from light source. Thisparticular standard curve was obtained witha phototransistor hanging down from the tipof a kymograph muscle lever into a shortlength of PVP pipe. A 3 volt miniaturelamp was ositioned at the bottom of thepipe to serve as a light source.

fers instructors the opportunity to up-grade undergraduate physiology labo-ratory instrumentation in a cost effec-tive manner without sacrificing thequality of the laboratory learningexperience.

REFERENCES1. Coyne, MD. Substitution of apple com-

puters for storage oscilloscopes andpolygraphs. Computers In Life ScienceEducation 2:81-86, 1985.

2. Esch HE, and JD McMillen. Microcom-puters in undergraduate physiologylaboratories. Collegiate Microcomputer2:151-157, 1984.

3. Kolitsky, MA. Microcomputer applica-tions in biology: using your Apple IIplus or lie as a laboratory instrument.Proceedings of the Western EducationalComputing Conference. pp193-197,1984.

4. Olivo, RF. Selecting an analog-digitalinterface: a tutorial. Computers In LifeScience Education 3:89-93, 1986.

01988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENCE EDUCATION 0742-3233/88/10.00 + 2.00

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CLSE 1987-88 COLLEAGUE DIRECTORY - PART IIIThe primary goal of the National Re-source for Computers in Life ScienceEducation (NRCLSE) is to cultivatecollaborative efforts among life sciencefaculty interested in using the computeras a teaching tool.

The listing that follows is a continua-tion of the 1987-88 directory publishedin the February and March issues ofCLSE. It is updated from the 1986-87directory and was drawn primarilyfrom respondents to questionnaires

PHYSIOLOGYABRAHAMS, V CDEPT OF PHYSIOLOGYQUEEN'S UNIVERSITYKINGSTON, ONTARIO K7L 3N6CANADA(613) 547-3022

AMLANER, CHARLES I JrDEPT OF ZOOLOGYUNIV OF ARKANSASFAYETTEVILLE, AR 72701

BASHOR, DAVID PDEPT OF BIOLOGYUNIV N CAROLINA ATCHARLOTTE

CHARLOTTE, NC 28223(704) 597-4047

BAUR, JOHN MDEPT OF BIOLOGYBRESCIA COLLEGE120 WEST SEVENTH STOWENSBORO, KY 42301(502) 685-3131 EXT 276

BENNETT, THOMAS EDEPT OF BIOLOGYBELLARMINE COLLEGENEWBURG ROADLOUISVII 7 E, KY 40205(502) 452-8198

BERG, VIRGINIADEPT OF BIOLOGYUNIV OF NORTHERN IOWACEDAR FALLS, IA 50614(319) 273.2770

BERGQUIST, BARTDEPT OF BIOLOGYUNIV OF NORTHERN IOWACEDAR FALLS, TA 50614(319) 273.2723

BEUMER, RONALD IBIOLOGY DEPTARMSTRONG STATE COLLEGESAVANNAH, GA 31419(912) 927-5314

BORQUEZ, DR RICARDO LEONUNIV AUTO - GUADALAJARALOMAS DEL VALLE 1ERASECC

GUADALAJARAMEXICO JAL 1-440

printed in CLSE. It is intended to helpreaders identify colleagues with com-mon interest areas.

The listings are arranged by the con-tent areas identified in response to thequestion, "What content areas do youteach?" As a result, entries may appearunder more than on heading.

Although every attempt has beenmade to ensure that the information iscurrent and correct, it is likely thatsome errors appear in this list, We

BROOKE, Dr JOHN DNEUROPHYSIOLOGY LABUNIVERSITY OF GUELPHSCHOOL OF HUMAN BIOLOGYGUELPH, ONTARIO N1G 2W1CANAD A(519) 824-4120 EXT 3416

BROWN, DAVID EDEPT OF ANIMAL SCIENCEUNIV OF NEVADA AT RENORENO, NV 89557-0004(702) 784-1629

BRUMLEVE. STANLEY JDEPT OF PHYSIOLOGYSCHOOL OF MEDICINEUNIV OF NORTH DAKOTAGRAND FORKS, ND 58202(701) 777-1 )74

BUCHHOLZ, ROBERT HDEPT OF BIOLOGYMONMOUTH COLLEGEMONMOUTH, IL 61462(309) 457-2350

BURNS, DR THOMAS ADEPT OF BIOLOGY &

MICROBIOLOGYNORTHWESTERN STATE UNIVNATCHITOCHES, LA 71497(318) 357-5323

CAMERON, DAVID GDEPT OF BIOLOGYMONTANA STATE UNIVBOZEMAN, MT 59717(406) 994-2670

CAMPBELL, KENNETHVCAPPWASHINGTON STATE UNIVPULLMAN, WA 99164-6520(509)335.6621

CAMPBELL, KEVIN PDEFT OF PHYSIOL & BIOPHYSUNIV OF IOWAIOWA CITY, IA 52240(319) 351-5833

CHEW, CATHERINE SDEPT OF PHYSIOLOGYMOREHOUSE SCHOOL OFMEDICINE

ATLANTA, GA 30310-1495(404) 752-1692

apologize in advance for any inconve-niences that may arise due to suchoversights.

If you are aware of other colleaguesthat should be listed, please send thepertinent information (name, address,phone number, BITNET address,teaching content area) to NRCLSE,Mail Stop RC-70, Univ. of Washing-ton, Seattle, WA 98195 or let us knowvia BITNET. NRCLSE's BITNETaddress is MODELL@UWALOCKE.

CHIEN, DR SHUDEPT OF PHYSIOL & BIOPHYSCOLUMBIA UNIV630 WEST 168TH STNEW YORK, NY 10032(212) 305-3686

CHRISTOFFERSON, JAY PDEPT OF BIOLOGYCALIFORNIA STATE UNIV ATSTANISI,.AUS

TURLOCK, CA 95380(209) 667-3476

CLARK, FRANCIS JDEPT OF PHYSIOLOGYUNIV OF NEBRASKA MED CfR42ND & DEWEY AVEOMAHA, NE 68105(402) 559-6478

CLARK, JAMES RDEPT OF ANIMAL SCIENCETEXAS TECH UNIVPO BOX 4169LUBBOCK, TX 79409-4169(806) 742-2469

COCKER-HAM, BILLFRESNO PACIFIC COLLEGE1717 S CHESTNUTFRESNO, CA 93702(209) 453-2045

CONAHAN, SHAWN TDEPT OF PHYSIOLOGYLOUISIANA STATE UNIV MED

CENTER1901 PERDIDO STNEW ORLEANS, LA 70112(504) 582-5783

CONSIGNY, D MACKEDEPT OF PH (SIOLOGYMEDICAL COLLEGE OF

PENNSYLVANIA3300 HENRY AVEPHILADELPHIA. PA 19129(215) 842-7059

CUBINA, JOSEFA MDEPT OF LIFE SCIENCESNEW YORK INST OF TECHOLD WESTBURY, NY 1156E(516) 686-7672

CUENA, EUGENIO MARTINDM DE BIOLOGIA ANIMAL

FACULTAD DE CIENCIASUNIVERSIDAD DE GRANADAGRANADA, SPAIN202212EXT 243

DE JONG, ALVIN ADEPT OF BIOL SCIENCECECAL POLYTECH STATE UNIVSAN LUIS OBISPO, CA 93407(805) 546-2209

DICKINSON, PROF C JOHNDEPT OF MEDICINEST BARTHOLOMEWS HOSPMEDICAL COLLEGE

WEST SMITHFIELD LONDON,ENGLAND EC1A7BE

DICKINSON, WINIFREDDEPT OF BIOLOGYUNIV OF STEUBENVILLESTEUBENVILLE, OH 43952(614) 283-3771

DIGGINS, MAUREENDEPT OF BIOLOGYAUGUSTANA COLLEGE29TH ST ANDS SUMMITSIOUX FALLS, SD 57197(605) 336-4809

DOBSON. ALANDEPT OF PHYSIOLOGYNYS COLLEGE OF VET MEDCORNELL UNIVMIACA, NY 14853(607) 253-3847

DOVE, LEWISDEPT OF BIOL SCIENCESWESTERN ILLINOIS UNIVMACOMB, IL 61455(309) 298-1166

DUGGAN, ELEANOR LBIOLOGY DEPTUNIV OF N CAROLINA ATGREENSBORO

ROOM 309, EBERHART BLDGGREDISBORO, NC 27412.5001(919) 379-5839

ELDER, DR BETTYDEPT OF BIOLOGYGEORGIA SOUTHWESTERNCOLLEGE

AMERICUS, GA 31709(912) 928-1250

0742.3233/88/S0.00+ 2.00 0 1988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENCEEDUCATION


EUSTIS-TURF, ELIZABETHBOX 694, MCV STATIONMEDICAL COLLEGE OFVIRGINIA

RICHMOND, VA 23298(804) 786-9824

FANNING, MARSHA EDEPT OF BIOLOGYLENOIR-RHYNE COLLEGEHICKORY, NC 28603(704) 328-7270

FERNIER, JOHN WDEPT OF BIOLOGYTHOMAS MORE COLLEGECRESTVIEW HILLS, KY 41017(606) 344-3374

FREED, DR JAMES MDEPT OF ZOOLOGYOHIO WESLEYAN UNIVDELAWARE, OH 43015(614) 369-4431 EXT 400

GRAY, F HARRIETHOLLINS COLLEGEBOX 9616HOLLINS COLLEGE, VA 24020(703) 362-6543

GREEN, JONATHANDEPT OF BIOLOGYROOSEVELT UNIVCHICAGO, IL 60605(312) 341-3676

GOOCH, VAN DDEPT OF SCIENCE &

MATHEMATICSUNIV OF MINNESOTAMORRIS, MN 56267(612) 589-2211

GRUENER, RAPHAELDEFT OF PHYSIOLOGYUNIV OF MARYLAND ATBALTIMORE

BALTIMORE, MD 21201(301) 528-2678

GWINN, DR JOHN FDEPT OF BIOLOGYUNIVERSITY OF AKRONAKRON, OH 44325(216) 375-7160

HAMPTON, JOHN KDEPT OF BIOL SCIENCESCAL POLYTECH STATE UNIVSAN LUIS OBISPO, CA 93407(805) 546 -2819

HECKENLIVELY, DONALD BDEFT OF BIOLOGYHILLSDALE COLLEGEHILLSDALE, MI49242(517) 437-7341

HEIDCAMP, WILLIAMDEPT OF BIOLOGYGUSTAVUS ADOLPHUSCOLLEGE

ST PETER, MN 56082(507) 931-7325

HEMPLING, HAROLD GDEPT OF PHYSIOLOGYMEDICAL UNIV S CAROLINACHARLESTON, SC 29425-2658(803) 792-3648

HILUARD, STEPHENDEPT OF BIOLOGYBALDWIN-WALLACE COLLBEREA, 01144017(216) 826-2265

HOUSE, EDWIN WDEPT OF BIOLOGYIDAHO STATE UNIVPOCATELLO, ID 83209(208) 236-3765

HOUSE, DR HERBERT WELON COLLEGEBOX 2270ELON COLLEGE, NC 27244-2010(919) 584-2294

JEGLA, THOMAS CDEPT OF BIOLOGYKENYON COLLEGEGAMBLER, OH 43022(614) 427 -2244

JENNINGS, MICHAEL LDEPT OF PHYSIOL & BIOPHYSUNIV OF IOWAIOWA CITY, IA 52242(319) 353-4035

JOHNSON, CLYDE EDEPT OF BIOL SCIENCESSOUTHERN UNIVERSITYBATON ROUGE, LA 70813(504) 771-5210

JOHNSON, DAVID WDEPT OF BIOLOGYCONCORDIA COLLEGEMOORHEAD, MN 56560(218) 299-3085

JOHNSON, IVAN MDEPT OF BIOLOGYCONCORDIA COLLEGEMOORHEAD, MN 56560(218) 299-3085

JOHNSON, WALTERNORTHWEST COLLEGE OF

CHIROPRACTIC2501 W 84TH STBLOOMINGTON, MN 55431(612) 888-4777 EXT 290

JONES, MICHAEL EDEPT OF ANATOMY &HISTOLOGY

FLINDERS UNIV OF SOAUSTRALIA

SCHOOL OF MEDICINEBEDFORD PARK,

SOUTH AUSTRALIA 5042(618) 275-9911 EXT 4281

JOYNER, RONALD WDEPT OF PHYSIOLOGYUNIV OF IOWAIOWA CITY, IA 52242(319) 353-7024

KALICHSTEIN, DENNISDEPT OF SCIENCEOCEAN COUNTY COLLEGETOMS RIVER, NJ 08753(201) 255-4000

KENDRICK,' EDEPT OF PHYSIOLOGYUNIV OF WISCONSIN134 SMI

MADISON, WI 53706(600 262 1465

KESSLER, SISTER IRMADEPT OF BIOLOGYCOLLEGE OF ST ELIZABETHCONVENT STATION, NJ 07961(201) 539-1600

LAGLER, REGISCRITICAL CARE DEPTMETHODIST HOSPITAL1701 N SENATE BLVDINDIANAPOLIS, IN 46202(317)929.5293

LAROCHELLE, JACQUESDEPT OF BIOLOGYLAVAL UNIVQUEBEC, PQCANADA G1K 7P4(418) 656-3187

McDONALD, DR GORDONDEPT OF BIOLOGYMcMASTER UNIV1280 MAIN ST WESTHAMILTON, ONTARIO LSS 4K1CANADA(416) 525-9140 EXT 4266

McHALE, PHILIP ADEPT OF PHYSIOL& BIOPHYSCOLLEGE OF MEDICINE,BMSB-357

UNIV OF OKLAHOMA HEALTHSCIENCES CENTER

PO BOX 26901OKLAHOMA CITY, OK 73190(405) 271-2316

MELNYCHUK, MARK SDEPT OF BIOL & ALLIED

HEALTH SCIENCEHART BLOOMSBURG UNIV

SCIENCE CENTERBLOOMSBURG, PA 17815(717) 389-4219

MESSICK, JOHN PDEPT OFIIIOLOGYMISSOURI SOUTHERN STATECOLLEGE

JOPLIN, MO 64801(417) 625-9376

MICHAEL, JOELDEPT OF PHYSIOLOGYRUSH MEDICAL COLLEGE1750 WEST HARRISON STCHICAGO, IL 60612(312)942.6426

MICKUS, JOHNDEPT OF BIOLOGYILLINOIS BENEDICTINE COLLLISLE, IL 60532-0900(312) 960-1500 EXT 509

MIKITEN, TERRY MGRADUATE SCHOOL OFBIOMEDICAL SCIENCES

UNIV OF TEXASSAN ANTONIO, TX 78284(512) 567-3709

MILLS, STEVEN HDEPT OF BIOLOGYCENTRAL MISSOURI STATE

UNIVWARRENSBURG, MO 64093

(816) 429-4933

MODELL, HAROLDDEPT OF RADIOLOGY RC-70UNIV OF WASHINGTONSEATTLE, WA 98195(206)548.6244

MORIARTY, C MICHAELDEPT OF PHYSIOL & BIOPHYSUNIV OF NEBRASKA MED CIROMAHA, NE 68105(402) 559.7282

OWF.N, LAWTONKANSAS WESLEYANSAUNA, KS 67401(913) 827-5541

NOCE/T[1,DR MRDEPT OF PHYSIOL & BIOPHYSCOLUMBIA UNIVNEW YORK, NY 10032(212)305.3636

ORTIZ, C LEODEPT OF BIOLOGYUNIV OF CALIFORNIASANTA CRUZ, CA 95064(408) 429-2247

PALMER, KENNETH CDEPT OF PATHOLOGYWAYNE STATE UNIV SCHOOLOF MEDICINE

DETROIT, MI 48201(313) 577-5152

PARRISH, DR JOHNDEPT OF BIOLOGYEMPORIA STATE UNIVEMPORIA, KS 66801(316) 343-1200

PETERSON, NITSDEPT OF COMPUTER &INFORMATION SCENCE

UNIV OF OREGONEUGENE, OR 97403-1201(503)686.3989

QUINN, DAVID LDEPT OF BIOLOGYMUSKINGUM COLLEGENEW CONCORD, OH 43762-1199(614) 826-8225

RAM, JEFFREY LDEPT OF PHYSIOLOGYWAYNE STATE UNIV SCHOOLOF MEDICINE

DETROIT, M1 48201(313) 577-1558

REINKING, LARRY NDEPT OF BIOLOGYMILLERSVII I F UNIVMILLERSVILLE, PA 17551(717) 872-3792

RIESEN, JOHNDEPT OF ANIMAL SCIENCEUNIV OF CONNECTICUT U-40STORRS, Cr 06268(203)486-2541

ROBBINS, DR HERBERT CDALLAS BAPTIST UNIV7777 WEST KIEST BLVDDALLAS, TX 75211.9800(214) 333.5302

©1988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENCE EDUCATION 0742-3233/88/50.00 + 2.00

3a


ROE, WALTER E ROLLINS COLLEGE DEPT OF BIOLOGY MILLERSVI7 '1 E, PA 17551DEFT OF VET PHYSIOL SCI WINTER PARK, FL 32789 ST FRANCIS XAVIER UNIV (717) 872-3339UNIV OF SASKATCHEWAN (305) 646-2433 ANITGONISH, NOVA SCOTIASASKATOON, SK S7N OWO CANADA B 2G 1C0 PSYCHIATRYCANADA STEIN, PAUL (902) 867-2294 PARISI, ANIIIONY J(306) 966-7371 DEPT OF BIOLOGY WRIGHT STATE UNIV SCHOOL

WASHINGTON UNIV WILMA, DALLAS OF MEDICINEROSELLI, ROBERT J ST LOUIS, MO 6313 0 DEPT OF BIOLOGY DAYTON, OH 45401-0927DEPT OF CHEM ENGINEERING (314) 889-6824 HASTINGS COLLEGE (513)376.6711VANDERBILT UNIV HASTINGS, NE 68901BOX 36 STATION B STENROOS, ORRIE 0 (402) 463-2402 EXT 264 RESEARCH TECHNIQUESNASHVILLE, TN 37235 DEPT OF BIOLOGY GLYNIS, ASU(615) 322-2602 LYNCHBURG COLLEGE WILLIAMS, W E MCW LIBRARIES

1501 LAKESIDE DR DEPT OF BIOLOGY 8701 WATERTOWN PLANK RDROTIIE, DR CARL F LYNCHBURG, VA 24501 TRINITY COLLEGE MILWAUKEE, WI 53226DEPT OF PHYSIOL &BIOPHYS (804) 522.8362 HARTFORD, CT 06106 (414) 257-8323INDIANA UNIV SCHOOL OF (203) 527-3151 EXT 570MEDICINE STOUT. JOHN MESSICK. JOHN P635 BARNHILL DRIVE DEPT OF BIOLOGY WILLISTON, JOHN S DEPT OF BIOLOGYINDIANAPOLIS, IN 46223 ANDREWS UNIV DEPT OF BIOLOGY MISSOURI SOUTHERN STATE(317) 264-8250 BERRIEN SPRINGS, MI 49104 SAN FRANCISCO STATE UNIV COLLEGE

(616) 471-32/43 SAN FRANCISCO, CA 94132 JOPLIN, MO 64801ROVICK, ALLEN A (415)584-5319 (417)625.9376DEPT OF PHYSIOLOGY SULAKHE, DR P VRUSH MEDICAL COLLEGE DEPT OF PHYSIOLOGY WILSON, DR MARLENE M SEROLOGYCHICAGO, IL 60612 COLLEGE OF MEDICINE UNIV OF PORTLAND STANSFIELD, WILLIAM(312)942.6567 UNIV OF SASKATCHEWAN PORTLAND, OR 97203 DEPT OF BIOL SCIENCE

SASKATOON, SK SiN OWO (503) 283-7123 CAL POLYTECH STATE UNIVRUF, DR KURT B CANADASAN LUIS OBISPO, CA 93407DEPT OF PHYSIOL & BIOPHYS (306) 966-6534 WOLF, MATTHEW B (805)546.2875DALHOUSIE UNIV DEPT OF PHYSIOLOGY

HALIFAX, NOVA SCOTIA SWEET, HAVEN SCHOOL OF MEDICINE STATISTICSCANADA 13311 4H7 DEPT OF BIOLOGY UNIV S CAROLINA BENEDICT, DR SUSAN(902) 424-3517 UNIV OF CENTRAL FLORIDA COLUMBIA, SC 29208 209 MOUNTAIN RIDGE DRIVEORLANDO, FL 32816 (803) 733-3241 HUNTSVILLE, AL 35801SAGE, MARTIN (305) 275-2922

(205) 536-7101DEPT OF BIOLOGY WOLLIN, AUNIV OF MISSOURI AT TAYLOR, JAMES WM DEPT OF PHYSIOLOGY BROOKE, DR JOHN DST LOUIS KALAMAZOO VALLEY COLLEGE OF MEDICINE NEUROPHYSIOLOGY LAB8001 NATURAL BRIDGE RD CONSIUNITY COLLEGE UNIV OF SASKATCHEWAN UNIVERSITY OF GUELPHST LOUIS, MO 63121 KALAMAZOO, MI 49009 SASKATOON, SK 57N OWO SCHOOL OF HUMAN BIOLOGY(314) 55305218 (616) 372-5356 CANADA GUELPH, ONTARIO N1G 2W1

CANADAscPimon, THOMAS I TESKEY, SISTER NANCY WUNDER, CHARLES C (519) 824-4120 EXT 3416DEPT OF PHYSIOL & BIOPHYS HOLY NAMES COLLEGE PHYSIOLOGY RESEARCH LABUNIV OF IOWA OAKLAND, CA 94619 UNIV OF IOWA CHEW, FSIOWA CITY, IA 52242 (415) 436-0111 OAKDALE, IA 52319 DEPT OF BIOLOGY(319) 353-4018 (319) 3534704 TUFTS UNIV

THIRUMALAL CHARLES HR MEDFORD, MA 02155SCHNERVEISS, JEANNETTE W DEPT OF PHYSIOLOGY YORK, DR DONALD H (617) 381-3189 EXT 3195DEPT OF BIOLOGY UNIV WEST INDIES DEPT OF PHYSIOLOGYHOFSTRA UNIV KINGSTON 7, JAMAICA UNIV OF MISSOURI AT CLARK, JAMES RHEMPSTEAD, NY 11550 WEST INDIES COLUMBIA DEPT OF ANIMAL SCIENCE(516) 560-5521 (809) 927-2090 COLUMBIA, MO 65212 TEXAS TECH UNIV

(314) 8S2 -7168 LUBBOCK. TX 79409-4169SIEGMAN, MARION TURNELL, M J (806) 742-2469DEPT OF PHYSIOLOGY DEPT OF ZOOLOGY YOUNG. BRUCE AJEFFERSON MEDICAL COLL UNIVERSITY OF ALBERTA DEPT OF ANIMAL SCIENCE CODY, RONALD1020 LOCUST ST CW 312 BIOL SO BLDG UNIVERSITY OF ALBERTA DEPT OF ENVIRON &PHILADELPHIA, PA 19107 EDMONTON, ALBERTA EDMONTON, ALBERTA COMMUNITY MEDICINE(215) 928 -7893 CANADA T6G 2E9 CANADA T6G 2135 RUTGERS MEDICAL SCHOOL

(403) 432-0665 (403) 432-3233 PISCATAWAY, NJ 08854SIMPSON, R J(201) 463.4490DEPT OF BIOLOGY VAN AMBURG, GERALD L ZEITTER, SHIRLEY

UNIV OF NORTHERN IOWA DEPT OF BIOLOGY DAVENPORT COLLEGE FASH1NG, NORMAN JCEDAR FALLS, IA 50614 CONCORDIA COLLEGE 415 EAST FULTON DEPT OF BIOLOGY(319) 273-2645 MOORHEAD, MN 56560 GRAND RAPIDS, MI 49503 COLLEGE OF WILLIAM(218) 299-3085 (616) 451-3511 AND MARYSTNNAMON, WALT

WILLIAMSBURG, VA 23185CENTRAL WESLEYAN COLL WALKER, RICHARD ZIMMERMAN, JAYCENTRAL, SC 29630 STERLING COLLEGE DEPT OF BIOL SCIENCES HAGEN, STANLEY VON(803) 639-2453 EXT 356 STERLING, KS 67579 ST JOHN'S UNIV DEPT OF PHARMACOLOGY

(316) 278-2173 GRAND CENTRAL & NEW 'JERSEY MEDICAL SCHSLAYMAN, C L UTOPIA PKWS NEWARK, NEW JERSEY 07103DEPT OF PHYSIOLOGY WATROUS, JAMES NEW YORK, NY 11439 (2m) 456-4385YALE SCHOOL OF MEDICINE DEPT OF BIOLOC" (718) 990-6161 EXT 5233NEW HAVEN, CT 06510-8026 ST JOSEPH'S UNIV RIPPEY, ROBERT(203) 788 -4478 PHILADELPHIA, PA 19131 PLANT PHYSIOLOGY RHE AM034

(215) 879-7342 STEUCEK, DR GUY L UNIV CONN HMI CTRSMALL,JAMES W DEPT OF BIOLOGY FARMINGTON, CI' 06073DEPT OF BIOLOGY WEISBART, DR MELVIN MILLERSVILLE UNIV (203) 674-2353

0742-3233/8840.00 + 2.00 ©1988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENCEEDUCATION

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SCHWAR7Z, ORLANDO A PINEVII.LE, LA 71359 SIOUX FALLS, SD 57197 CANADA M5S IA IDEPT OF BIOLOGY (318) 487-7611 (605) 336-4809 (416) 978-3506UNIV OF NORTHERN IOWACEDAR FALLS, IA 50614 BUCHHOLZ, ROBERT H GAMS, ROGER MURPHY, GEORGE(319) 273-2106 DEPT OF BIOLOGY DEPT OF BIOL SCIENCE MEDDLE TENN STATE UNIV

MONMOUTH COLLEGE CAL POLYTECH STATE UNIV MURFREESBORO, TN 37132TAUB, STEPHAN R MONMOUTH, IL 61462 SAN LUIS OBISPO, CA 93407 (615) 898-2847DEPT OF BIOLOGY (309) 457-2350 (805) 546-2551GEORGE MASON UNIV OWEN, LAWTONFAIRFAX, VA 22030 CARICO, JAMES E GIBSON, LINDA KANSAS WESLEYAN(703) 323-2181 DEPT OF BIOLOGY DEPT OF BIOLOGY SALINA, KS 67401

LYNCHBURG COLLEGE EASTERN WASHINGTON UNIV (913) 827-5541WERNER, I IURWIN LYNCHBURG, VA 24501 CHENEY, WA 99004DEFT OF BIOLOGY (804)522.8366 (509) 359-2845 SINGLETARY, ROBERT LNORTHERN MICHIGAN UNIV DEPT OF BIOLOGYMARQUETTE, MI 49855 COOCERHAM, BILL GRAY, F HARRIET UNIV OF BRIDGEPORT(906)227-2310 FRESNO PACIFIC COLLEGE HOLLINS COLLEGE, BOX 9616 BRIDGEPORT, CT 06601

1717 S CHESTNUT HOLLINS COLLEGE, VA 24020 (203) 576-4265TAXONOMY FRESNO, CA 93702 (703) 362-6543

WALTOS, DIRK R (209)453.2045 SINNAMON, WALTDEPT OF BIOLOGY HILTON, DONALD F7 CENTRAL WESLEYAN COILCAL POLYTECH STATE UNIV DE JONG, ALVIN A DEPT OF BIOL SCIENCES BOX 443SAN LUIS OBISPO, CA 93406 DEPT OF BIOL SCIENCE BISHOP'S UNIVERSITY CENTRAL, SC 29630(805) 546-2721 CAL POLYTECH STATE UNIV LENNOXVILLE, QUEBEC (803) 639-2453 EXT 356

SAN LUIS OBISPO, CA 93407 CANADA JIM 1Z7VIROLOGY (505) 546-2209 (819)569.9551 STANTON, GEORGE E

BOND, CLIFFORD DEPT OF BIOLOGYDEPT OF MICROBIOLOGY DICKINSON, WINIFRED LEONG, XINGSTON COLUMBUS COLLEGEMONTANA STATE UNIV DEPT OF BIOLOGY DE2T OF BIOL SCIENCE COLUMBUS, GA 31993BOZEMAN, MT 59717 UNIV OF STEUBENVILLE CAL POLYTECH STATE UNIV (404) 568-2065(406) 994 -4130 STEUBENVILLE, OH 43952 SAN LUIS OBISPO, CA 93407

(614) 283-3771 (805) 5462788 STEVENS, E DZOOLOGY DEPT OF ZOOLOGY

BLACK, JOE B DIGGINS, MAUREEN MACHIN, DR JOHN UNIV OF GUELPHDEPT OF BIOLOGY DEPT OF BIOLOGY DEPT OF ZOOLOGY GUELPH, ONTARIOLOUISIANA COLLEGE AUGUSTANA COLLEGE UNIV OF TORONTO CANADA NIG 2W1LOUISIANA COLLEGE STA 29TH ST AND S SUMMET TORONTO, ONTARIO (519) 824-4120 EXT 2137

SUBSCRIPTION INFORMATION Computers in Life Science Education is published monthly by National Resource forComputers in Life Science Education, Mail Stop RC-70, University of Washington, Seattle,WA 98195. Subscription rate is $30.00 for 12 issues, including postage and handling in theUnited States and Canada. Add $20.00 for postage (airmail) in Mexico and Europe and$23.00 for the rest of the world.

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1988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENCE EDUCATION 0742-3233/8840.00 + 2.00

33

VOLUME 5 NUMBER 5, MAY 1988 CLSEE3 5(5)33-40, 1988 ISSN 0742-3233

I. Ar 1111111111111111111111111111111111111

HAROLD I. MODELLDepartment of RadiologyUniversity of WashingtonSeattle, Washington






TERRY M. MIKITENGraduate School of Biomedical SciencesUniversity of Texas Health Science CenterSan Antonio, Texas

JAMES E. RANDALLDepartment of PhysiologyIndians UniversityBloomington, Indiana

PATRICIA SCHWIRIANCollege of NursingOhio State UniversityColumbus, Ohio



for Biomedical CommunicationsNational Library of MedicineBethesda, Maryland


GLEN YOSHIDADepartment of life SciencesLos Angeles Southwest CollegeLos Angeles, California

NRCLSE

CONTENTS

HYPERCARD WHAT IS IT?Dorothy Wooley-McKay

WHERE'S THE SOFTWARE?

33

35

HYPERCARD WHAT IS IT?Dorothy Wooley-McKayDepartment of Biology, Glendale Community College, Glendale, Arizona

Many individuals familiar with theApple Macintosh microcomputer aretalking about what some believe is arevolutionary new product. The prod-uct is called HyperCard, and it was re-leased by Apple Computer at the Mac-World Expo in Boston in August,1987.

WHAT IS HYPERCARD?This is a difficult question to answer

because it is many things to differentpeople. The author of HyperCard, BillAtkinson (also author of MacPaint),has called HyperCard a "software erec-tor set". Presumably, it is a means bywhich individuals who have never pro-grammed a computer can now do so.However, HyperCard is more than that.

HyperCard is based on the concept ofhypertext. Hypertext is a system oftransferring information by means of acomputer rather than books or other

print media. Some, of course, will notlike this manner of information trans-fer. However, it has some advantages.

What does HyperCard have to dowith using computers in the life sci-ences? Consider the following situa-tion. A student is studying the electri-cal activity of the heart. In order to un-derstand the principles, he must be fa-miliar with electrophysiology, includ-ing the concepts of depolarization andrepolarization. Of course, the studenthas a textbook and lecture notes to useas reference material. However, thesections on electrophysiology werecovered in the previous semester, andthe student has forgotten much of whatwas learned in those sections. He mustgo back and review that material. Un-fortunately, the student cannot find thelecture notes from the previous semes-ter, There is still the textbook, but thisstudent never opened the textbook. He

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34 COMPUTERS IN LIFE SCIENCE EDUCATION, VOLUME 5, NUMBER 5, MAY 1988

used only lecture notes to study be-cause the exams were based on thelecture notes, and he doesn't knowwhere to find the textbook on electso-physiology. This, of course, is an ex-treme case. However, using Iiyper-Card or another form of hypertext, wecan create the following scenario.

This same student is studying theelectrical activity of the heart usingHyperCard stacks. HyperCard soft-ware consists of stacks. The analogy isa stack of cards. Each stack is dividedinto cards that are accessed one at atime. On each card, there are fields fortext, graphics and buttons (see Figure1). Part of the heart stack is a tutorialincluding text (read from the computermonitor) and graphics. When the stu-dent gets stuck with the electrophysio-logical concepts, he clicks (with themouse) on a button located on one ofthe cards of the heart stack. When heclicks on this particular button, he isimmediately shown a stack on basicelectrophysiology. In this stack theremay be a simulation of an action poten-tial in a nerve fiber as well as text andgraphics explaining the principles ofelectrophysiology. When the studenthas finished with electrophysiology, heclicks on another button and goes backto the heart. The same process couldbe used to connect the anatomy of theheart with the physiology of the heart.For example, when studying the Pur-kinje system, the student could click onPurkinje system and go to a card illus-trating the location of this system. Allof the information is linked together inan intuitive way. Instead of informa-tion being presented in a serial form, itcan be linked together in a way morelogical to human thought.

EXAMPLE APPLICATIONSThe following applications for teachinglife sciences using HyperCard weredescribed in the March 29th issue ofMacWeek magazine.

Interactive Medical Record This wasdeveloped by Dr. Ed' and Shultz atDartmouth Medical School. Usingthis stack, a student can, among otherthings, point to a patient's chest and

hear the digitized sound of a heartmurmur. In addition, the studentscan click on buttons to see vital signs,X-Rays, past visits, and blood smears.There is also a glossary, so siudentscan click on any word and look it upin the glossary.

Electric Cadaver This application,developed by Drs. Robert Chase andSteven Freedman of Stanford Univer-sity Medical School presents anatomi-cal drawings on the monochromemonitor of a Mac II. When a studentclicks on a part of the image, thatportion is presented on the screen. Inaddition, color photographs of thosestructures are shown on a video moni-tor. The color photographs are takenfrom the Basset series of 3 dimen-sional anatomical drawings.

(HyperCard can access videodiscsthat contain color photographs ofalmost anything. The student canthus move easily from a large view ofan anatomical structure to more de-tailed aspects of the same structure.)

r a File Edit Go Tools Objects

MACIlnical This is a project atGeorgetown University MedicalSchool. At the present time, there aretwo HyperCard stacks being used,HypeRite Up and a stack teaching theanatomy of the hand.

The spectrum of possibilities usingHyperCard in education is almost limit-less. There is the ability to incorporatedigitized sound, such as heart murmurs(realistic sounds, not computer sounds)into HyperCard stacks. HyperCardstacks can be linked to videodiscs.HyperCard stacks can also be linked toreal time animations using Video-Works. Students can easily jump fromone subject to a related subject. Hyper-Card stacks can be made as tutorials,simulations or addenda to laboratoryexercises. Eventually, a large data baseof any kind of information can bestored on CD ROM players and acces-sed with HyperCard.

If I wax eloquent on the possibilitiesof using HyperCard in education, it isbecause I think the potential of Hyper-Card has barely been tapped.

SproutorwavowikowettniamoutoomprommnowanommoinannuTommoneznautcrx3:581

Master Index Card

Area for Text

FIGURE 1. Example of fields available on HyperCard cards.

©1988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENCE EDUCATION 0742.3233/88/S0.00 + 2.00

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COMPUTERS IN LIFE SCIENCE EDUCATION, VOLUME 5, NUMBER 5, MAY 1988 35

WHERE'S THE SOFTWARE?

In the past, we have published lists oflife science software sources and pro-grams available through them. Thefollowing list is presented as the latestin a continuing effort to make col-leagues aware of potential resources.As in the past, no attempt has beenmade by NRCLSE to review thesematerials.This month's listings are arranged by

content area. Each item includes avendor code relating' the software to thevendors list appearing on page 39.

If you have found specific softwarehelpful in your teaching efforts, pleaseshare your good fortune by letting usknow about the program(s) and sup-plier(s) so that we can make this infor-mation available through future soft-ware lists. Send pertinent informationto Dr. Harold Modell, NRCLSE, MailStop RC-70, University of Washington,Seattle, WA 98195 or send us a noteon BITnet. Our BITnet address isMODELL@UWALOCKE.

ANATOMYGROSS ANATOMY TUTORIAL

Tutorial for gross anatomy review byregion a:td for self-test in NationalBoard format. Program available forApple II equipment. L.1HEART LAB

Animated graphics simulation ofhuman heart. Program available forApple II, TRS-80 Models I and III,PET, and Atari 800/800XL equipment.E.1HUMAN ANATOMY PICTURE FILE

Hi-Res diagrams of heart, brain, eye,ear, respiratory system, kidney, endo-crine system, neurons, circulatory sys-tem schematic, and digestive system.Program available for Apple II equip-ment. D.1LOCOMOTION

Tutorial reviewing types and func-tions of bones and muscles. Programavailable for Apple II and TRS-80Model III equipment. J.1

BIOCHEMISTRY

ANIMATIONSContains animations for demon-

strating DNA structure and synthesis,RNA structure and synthesis, andprotein synthesis. Program availablefor Apple II equipment. E.3BIOCHEMISTRY

Tutorial covering basic atomic struc-ture, balancing equations, and proper-ties of proteins and carbohydrates.Program available for Apple II andTRS-80 Model III equipment. 3.1DNA STRUCTURE AND SYNTHESIS

Tutorial dealing with nucleotidestructure and linkage between nucleo-tide, base complementarity and hydro-gen bonding. Program available forApple II equipment. E.3ENZKIN: Enzyme Kinetics

Simulation of enzyme-catalyzedreactions. Program available for AppleII equipment. C.4ENZLAB

Simulations for designing and car-rying out enzyme kinetics experiments.Program available for Apple II andIBM-PC compatible equipment. B.1ENZPACK

An enzyme kinetics teaching and cal-culation program. Program availablefor Apple II and IBM-PC compatibleequipment. B.1ENZYME ACTION

Tutorial on the basic nature and func-tion of enzymes. Program available forApple II equipment. B.1ENZYME-SIMULATION, ENZYMEACTION

Simulation of enzyme action and theeffects of inhibitors using acetylcholin-esterase. Program available for AppleII, TRS-80 Models I and III, IBM-PC,and Commodore 64/128 equipment.D.2GENE MACHINE

Tutorial/Simulation dealing withDNA replication and protein synthesis.Program available for Apple IIequipment. H.1MOLECULAR BIOLOGY SERIES

Programs demonstrating central proc-esses of RNA and protein synthesis and

DNA synthesis and repair. Programavailable for Apple II and IBM-PCcompatible equipment. B.1MOLGRAF

Molecular graphics package.Program available for Apple II andIBM-PC compatible equipment. B.1THE NUCLEIC ACIDS

Tutorial/Simulation dealing withprincipal nucleotides and synthesis ofRNA. Program available for Apple IIequipment. E.2PROTEIN SYNTHESIS

Tutorial dealing with the generalstructure of the amino acids and forma-tion of peptide bonds. Program avail-able for Apple II equipment. E.3RNA STRUCTURE AND SYNTHESIS

Tutorial extending the concept ofhydrogen bonding between comple-mentary bases to show the synthesis ofRNA on the DNA template and theanalogies in structure between DNAand RNA. Program available forApple II equipment. E.3

BIOLOGYADAPTION AND IDENTIFICATION

Tutorial covering animal adaptationto different environments and animalidentification. Program available forApple II equipment. S.1ANIMAL REPRODUCTION

Tutorial reviewing sperm develop-ment, egg and fertilized egg. Programavailable for Apple II and TRS-80Model III equipment. J.1ASEXUAL REPRODUCTION

Tutorial reviewing cell division.Program available for Apple II andTRS-80 Model III equipment. 3.1BIOLOGY COMPUTERSTIMULATIONS

Tutorial covering various biologicalconcepts and experimental areas in-cluding enzymes, photosynthesis, res-piration, diffusion, meiosis, muscles,nerves, and genetics. Program avail-able for Apple II and TRS-80 Models Iand III equipment. L.1DIFFUSION AND ACTIVETRANSPORT

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Tutorial covering diffusion, osmosis,and active transport in biological sys-tems. Program available for Apple IIequipment. S.2ENERGETICS AND A .TABOLISM,GARDEN OF BIOLOGY: VOLUME I

Data base illustrating reactions ofmetabolism and interactions betweenthe several metabolic compartments ofa cell. Program available for Macin-tosh equipment. K.1EVOLUTION, GARDEN OFBIOLOGY: VOLUME 2

Data base illustrating relations amongorganisms of many kinds, emphasizingthe history and mechanics of their evo-lutionary change. Program availablefor Macintosh equipment.KNOWLEDGE MASTER - BIOLOGY 2

Test-item database for test generation.Content covers coelenterates, arthro-pods, insects, fish, amphibians, andreptiles. Part of a 5-program Biologyseries for Apple II equipment. A.1KNOWLEDGE MASTER - BIOLOGY 3

Test-item database for test generation.Content covers birds, mammals, pri-mates, protists, bacteria and taxonomiczoology. Part of a 5-program Biologyseries for Apple II equipment. A.1OSMO- OSMOSIS IN RED BLOODCELLS

Simulation of red blood cells in hy-pertonic, hypotonic, and isotonic solu-tions. Program available for Apple II,TRS-80 Model III, IBM-PC, andCommodore 64/128 equipment. D.2OSMOSIS AND DIFFUSION

Tutorial/Simulation covering effectsof temperature, concentration, solubil-ity, molecule size and charge, andmembrane pore size on flow of matteracross semi-permeable membranes.Program available for Apple II, TRS-80 Models I and III equipment. E.2OSMOTIC PRESSURE

Simulation of thistle tube experimentsand animation of a molecular model forosmosis. Program available for AppleII equipment. C.4PASSIVE TRANSPORT

Tutorial-simulation covering diffu-sion and osmosis. Program for MS-DOS compatible equipment. C.1SIMULATION OF HEMOGLOBINFUNCTION

Simulations of hemoglobin andmyoglobin functions. Program for

Apple II equipment. C.2

BOTANYALGAL GROWTH

Simulation of the effects of eight vari-ables on growth of algae. Programavailable for Apple II and IBM-PCcompatible equipment. 0.1BIOLOGY FRUIT KEY

Identifies 125 trees and shrubs.Program available for Atari 400/800equipment. D.3COMPETE:Plant competition

Simulation of experiments involvinginteraction between flowering plants.Program available for Apple II equip-ment. C.4FAMILY IDENTIFICATION

Data retrieval program to review thecharacteristics of 74 North Americanflowering plant families. Programavailable for Apple II equipment. C.4LEAF: STRUCTURE ANDFUNCTION

Tutorial-simulation covering the anat-omy and physiology of the leaf withrespect to its role as the "chemical fac-tory" of the plant. Program for IBM-PC (PC-DOS). C.1PIIOTOSYNTHESIS AND LIGHTENERGY

Simulation focuses on characteristicsof light and its role as an energysource. Program for IBM-PC (PC-DOS). C.1PIIOTOSYNTIIESIS ANDRESPIRATION

Demonstration and simulations focus-ing with light and dark reactions ofphotosynthesis, respiration, and ATPcycle. Program available for Apple IIequipment. S.1PHOTOSYNTHESIS & TRANSPORT

Tutorial dealing with photosynthesisand transport in plants. Program avail-able for Apple II and TRS-80 Model IIIequipment. J.1PLANT ANATOMY PICTURE FILE

Hi-Res diagrams of roots, stem cross-section, leaf cross-section, photosyn-thesis, flowers, seeds, and germination.Program available for Apple IIequipment. D.1PLANT GROWTH

Tutorial-simulation covering physiol-ogy of growth beginning with the seed.Covers hormone control, feedbackmechanisms, transport, and differentia-

don. Program for IBM-PC (PC-DOS).C.1PLANT-PLANT GROWTHSIMULATION

Simulation of the effects of lightintensity and duration on growth anddevelopment of green plants. Programavailable for Apple II, TRS-80 ModelsI and III, IBM-PC, and Commodore64/128 equipment. D.2REPRODUCTION IN PLANTS

Tutorial reviewing asexual and sexualreproduction in plants. Program avail-able for Apple II and TRS-80 Model IIIequipment. J.1SOLAR FOOD

Tutorial/Simulation dealing withphotosynthesis. Program available forApple II equipment. H.1

CYTOLOGYCELL CHEMISTRY I

Tutorial covering various chemicalstructures. Program available forApple II and IBM-PC compatibleequipment. S.3CELL CHEMISTRY II

Tutorial covering the chemical andphysical processes that occur withincells. Program available for Apple IIand IBM-PC compatible equipment.S.3CELLGROWSimulation of cell kinetics. Program

available for Apple II equipment. U.1CELL GROWTH AND MITOSIS

Interactive simulation covering sur-face area-volume ratio, chromosomenumber, chromosome replication, andcytoplasmic division. Program forIBM-PC (PC-DOS). C.1CELLS: STRUCTURE ANDFUNCTION

Simulation reinforces basic conceptsof cell structure, cell functions, waterMovement and concentration gradients,and diffusion and active transport. Pro-gram available for Apple II equipment.S.1

ECOLOGYAIR POLLUTION

Simulation of carbon monoxide pol-lution in an urban environment. Pro-gram available for Apple II and TRS-80 Model I and III equipment. E.2AQUATIC ECOLOGY

Utilities to perform many of the cal-

©1988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFESCIENCE EDUCATION07423233/8840.00 + 2.00


culations common to aquatic ecology.Program available for Apple II andIBM-PC equipment. 0.1AQUATIC ECOLOGY DATASIMULATION

25 simulations covering aquatic sys-tems. Program available for Apple IIand IBM-PC equipment. 0.1ECOLOGICAL DATA SIMULATION

25 simulations covering ecologicalsystems. Program available for AppleH and IBM-PC equipment. 0.1ECOLOGY

Rote drill reviews and reinforces con-cepts of general terrestrial, and aquaticecology. Available for Apple H andIBM-PC compatible equipment. S.3ECOLOGY

Simulation dealing with plant popula-tion sizes and growth pattern. Avail-able for Apple II equipment. S.1I.:COLOGICAL ANALYSIS - PC

Utilities that perform life table anal-ysis, interspecific association indices,community similarity, diversity in-dices, descriptive statistics, mark-release recapture analysis, plus regres-sion and correlation analysis. Programavailable for IBM-PC compatibleequipment. 0.1ECOLOGICAL ANALYSIS VOL. 2 -PC

Utilities that perform community sim-ilarity analysis, indices of dispersion.species-area curve, and step-wise mult-iple regression. Program available forIBM-PC compatible equipment. 0.1ECOLOGICAL ANALYSISPROGRAMS PLUS

Utilities that perform life table analy-sis, community similarity indices,diversity indices, predator-prey model-ing, mark-recapture analysis, descrip-tive statistics, plus regression and cor-relation analysis. Program availablefor Apple II equipment. 0.1ECOLOGICAL MODELING

Series of 7 programs dealing with avariety of techniques for modeling eco-logical systems and processes. Pro-gram available for Apple II and IBM-PC compatible equipment. C.4NICHE-ECOLOGICAL GAME!SIMULATION

Game in which students attempt toplace an organism in its proper ecologi-cal niche correctly by specifying envi-ronment, range, and competitor. Pro-

gram available for Apple II, TRS-80Models I and III, IBM-PC, andCommodore 64/128 equipment. D.2POLLUTE

Simulation of factors affecting waterquality. Includes temperature, amountand type of pollutant, and water treat-ment. Program available for Apple II,PET/CBM, and TRS-80 Model IIIequipment. C.3POLLUTE:IMPACTIWATERPOLLUTANTS

Simulation of the impact of variouspollutants on typical bodies of water.Program available for Apple II, TRS-80 Models I and III, IBM-PC, andCommodore 64/128 equipment. D.2WATER POLLUTION

Simulation of the effects of tempera-ture, type of waste, dumping rate, andmethod of trutment on the impact ofpollution on aquatic life. Programavailable for Apple II, TRS-80 ModelsI and III equipment. E.2

EVOLUTIONEVOLUT: Evolution and NaturalSelection

Simulation of fluctuations in genefrequenceis of wild populations. Pro-gram available for Apple II equipment.C.4EVOLUTION

Simulations covering mutation, geneflow, natural selection, and geneticdrift on populations. Program avail-able for Apple I! and IBM-PC compat-ible equipment. 0.1

GENETICSADVANCED GENETICS

Tutorial/Simulation presented as anine-part program covering dominanceand recessiveness, partial dominance,lethality, mechanism of inheritance,multiple alleles, sex linkage, multi-traitinheritance, crossing over, and genemapping. Program available for AppleII equipment. E.2CATGEN

Simulation allowing students to matedomestic cats of known genotypes.Program available for Apple II andIBM-PC compatible equipment. C.4CATLAB (Second Edition)

Simulation in introductory genetics.Program available for Apple II andIBM -PC compatible equipment. C.4

CELLS AND GENETICS PICTUREFILE

Hi-Res diagrams of animal cell, plantcell, mitosis, meiosis, Punnett Square,sex linked traits, DNA replication, pro-tozoa, energy reactions, and pedigree.Program available for Apple II equip-ment. D.1DICROSS-DIHYBRID CROSSES

Simulation of various types of dihy-brid crosses. Program available forApple II, TRS-80 Models I and III,IBM-PC, and Commodore 64/128equipment. D.2DNA-THE MASTER MOLECULE

Simulation dealing with DNA struc-ture. Programs available for Apple IIequipment. E.2DNAGEN-DTIAIGENETIC CODESIMULATION

Simulation of genetic code to produceprotein sequences. Program availablefor Apple II, TRS-80 Models I and III,IBM-PC, and Commodore 64/128equipment. D.2FLYGEN

Simulation of monohybrid or dihybridcrosses with 25 varieties of Drosophila.Program available for Apple II, TRS-80 Models I and III, IBM-PC, andCommodore 64/128 equipment. D.2GENESIM

Simulations of experminents in bac-terial and molecular genetics. Programavailable for Apple II and IBM-PCcompatible equipment. B.1GENTIC DRIFT

Tutorial-simulation focusing on ran-dom changes with time in the distribu-tion of individuals in small popula-tions. Program for Apple II equipment.C.2GENETICS

Tutorial covering various crosses inplants arid fruit fly populations. Pro-gram available for Apple II and TRS-80 Model III and IV equipment. J.1GENETICS

Tutorial that allows students to ex-plore Mendel's experiments, PunnettSquares, sex linkage in fruit flies, andmultiple alleles. Program available forApple II equipment. S.1GENETICS

Tutorial examines DNA molecule andprogresses to applied genetics. Pro-gram availr,V,,e for Apple II and IBM-PC compatible equipment. S.3

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HEREDITY DOGTutorial covering various genetic top-

ics. Program available for Apple II andCommodore 64/128 equpiment. H.1HUMAN GENETIC DISORDERS

Simulation investigating inheriteddisorders. Program available for AppleH equipment. H.1INTRODUCTORY GENETICS

Three part tutorial covering a varietyof topics. Program available for AppleII, TRS-80 Models I and III equip-ment. E.2LIFE

Educational game dealing withchanging distributions of individuals.Program for Apple II equipment. C.2LINKOVER: Genetic Mapping

Simulation of genetic mappingexperiments. Program available forApple II equipment. C.4MEIOSIS

Tutorial/Simulation providing aninteractive portrayal of gamete forma-tion. Program available for Apple IIand IBM-PC equipment. E.2MEIOSIS, MITOSIS, PROTEINSYNTHESIS

Simulation demonstrating mitosis,meiosis, DNA rc Jlication, and proteinsynthesis. Program available for AppleII equipment. S.1MENDELIAN GENETICS

Simulation covering dominance,partial dominance, lethatlity, linkage,and sex linkage. Program for Apple IIequipment. C.2MONOCROS-MON011YBRIDCROSSES

Simulation of various monohybridgenetic crosses. Program available forApple II, TRS-80 models I and III,IBM-PC, and Commodore 64/128equipment. D.2NATURAL SELECTION

Tutorial/Simulation dealing withgenetics and e...olution to populations.Program available for Apple IIequipment. E.2POPGEN-POPULATION GENETICS

Simulation of the effects of Hardy-Weinberg Law conditions on gene,genotype, and phenotype frequenciesof a population over time. Programavailable for Apple JI, TRS-80 ModelsI and III, IBM-PC, and Commodore64/128 equipment. D.2

MICROBIOLOGYDILUTE-MICROBIAL DILUTIONSERIES

Simulation covering design andtesting of microbial dilution series todetermine concentration of a bacterialsolution. Program available for AppleII, TRS-80 Model III, IBM-PC, Com-modore 64/128 equipment. D.2MICROBIOLOGY TECHNIQUES

Tutorial/Simulation covering variouslaboratory procedures. Program avail-able for Apple II equipment. E.2

NEUROSCIENCENEUROMUSCULAR CONCEPTS

Tutorial covering muscle actionpotentials, use of electromyograph,=traction, muscle action and move-ment disorders. Program for Apple IIequipment. B.2

PHARMACOLOGYILEUM

Simulates laboratory experiments in-vestigating effects of drugs on the invitro guinea pig ileum. Programavailable for Apple II and IBM-PCcompatible equipment. B.1PRINCIPLES OF PIIARMACOLOGY

Tutorial covering history, drug ab-sorption and distribution, biotrans-formation and elimination, mechan-isms of action, and drug safety andefficacy. Program for Apple IIequipment. B.2

PHYSIOLOGYABGAME

Tutorial and game providing practicein acid-base principles. Program avail-able for IBM-PC compatible equip-ment. N.3ACID-BASE PIIYSIOLOGYSIMULATION

Simulation of acid-base disturbancesbased on Davenport Diagram. Pro-gram available for IBM-PC compatibleequipment. I.1BASIC HUMAN

Integrated systems model of humanphysiology. Program available forIBM-PC compatible equipment. R.1BIOFEEDBACK

Part of 10 program package Experi-ments in Human Physiology. Experi-ments include biofeedback, condi-

tioning, a^d perception measurements.Program available for Apple II equip-ment. H.1BIOFEEDBACK MICROLAB

Package includes a pulse rate sensorthat measures EMG, a thermistor probeto measure skin temperature, and aninterface circuit that enables student toconnect the sensors to the computer.Program available for Apple II andCommodore 64/128 equipment. H.1THE BODY IN FOCUS

Tutoria: for investigating body sys-tems including skeletal, muscular,respiratory, cardiovascular, gastrointes-tinal, endocrine, and integumentary.Available for Apple H and IBM-PCcompatible equipment. N.2CALIBRATION

Part of 10 program package Experi-ments in Human Physiology. Tem-perature and timing NI tions are cali-brated against standards. Programavailable for Apple II equipment. H.1CAPEXCH

Simulation dealing with exchange atthe capillary level. Available for IBM-PC compatible equipment. N.3CARDIOVASCULAR FITNESS LAB

Provides students with everythingthey need in order to use the microcom-puter to monitor cardiovascular activ-ity. Program available for Apple II andCommodore 64/128 equipment. H.1CARDIOVASCULAR INTERACTIONS

Cardiovascular Physiology simula-tion. Program available for IBM-PCcompatible equipment. I.1CARDIOVASCULAR PHYSIOLOGYPART I: PRESURE /FLOWRELATIONS

Tutorial dealing with a variety of cal-culations in the area of hemostatics/hemodynamics. Program available forIBM-PC compatible equipment. R.2CARDIOVASCULAR PHYSIOLOGYPART II: REFLEX

Tutorial dealing with carotid sinusregulation of blood pressure, and reflexresponses in hemorrhage and exercise.Program available for IBM-PC compat-ible equipment. R.2CIRCSIM: A TEACHING EXERCISEON BLOOD PRESSUREREGULATION

Simulated experiment based on amodel of the baroreceptor reflex loop.

0 1988 BY Ivi,.10NAL RESOURCE FOR COMPUTERS IN LIFE SCIENCE EDUCATION 0742.3233/8840.00 + 2.00


Program available for IBM-PCcompatiNe equioneni R.2CIRCSYST

Simulation of hcmodynamics.Available for IBM-PC compatibleequipment. N.3DIGESTION

Tutorial covering digestion insimple organisms and humans.Program available for Apple H andTRS-80 Model III equipment. J.1ENDOCRINE SYSTEM

Tutorial covering hormones, effectsand problems. Program available forApple II and TRS-80 Model IIIequipment. J.1EXCRETION

Tutorial reviewing metabolicwastes, waste removal, and kidneyfunction. Program available forApple II and TRS-80 Model IIIequipment. 1.1

VENDORS

A.1 Academic HallmarksP.O. Box 998Durango, CO 81301(303) 247.8738

B.1 BIOSOFT22 Hills RoadCambridge CB2 1JP, United Kingdom

P.O. Box 580Milltown, NJ 08850

B.2 Biosource Software2105 S. Franklin, Suite BKirksville, MO 63501(816) 665-3678

C.1 Classroom Consortia Media, Inc.57 Bay StreetStaten Island, NY 10301(800) 237-1113(800) 522-2210

C.2 COMpressP.O. Box 102Wentworth, NH 03282(603) 764-5831

C.3 Compuware15 Center RoadRandolph, NJ 07869(201) 366-8540

C.4 CONDUIT

The University of IowaOakdale CampusIowa City, IA 52242(319) 335.4100

D.1 Datatech Software Systems19312 East Eldorado DriveAurora. CO 90013

D.2 Diversified Education Enterprises725 Main StreetLafayette, IN 47901(317) 742-2690

D3 DYNACOMP, Inc.1064 Gravel RoadWebster, NY 14580(716) 671-6160(800)828.6772

E.1 Educational Activities, Inc.P.O. Box 392Freeport, NY 11520(800) 645-3739(516) 223.4666

E.2 Educational Materials andEquipment Co.

P.O. Box 17Pelham, NY 10803(914) 576-1121

E3 EduTech, Inc.303 Lamatinc StreetJamaica Plain, MA 02130(617) 524-1774

H.1 HRM Software175 Tompkins AvenuePleasantville, NY 10570(914) 769-7496(800) 431-2050

.Adiana University School of MedicineDepartment of Physiology and Biophysics635 Barnhill DriveIndianapolis, IN 46223

.I.1 J & S Software14 Vanderventer AvenuePort Washington, NY 11050(516)944.9304

K.1 Kinko's Service Corporation4141 State StreetSanta Barabara, CA 93110(800) 235-6919in CA (800) 292-6640outside USA (800) 967-0192

L.1 Life Science Associates1 Fennimore RoadBayport, NY 11705

(516) 472-2111

N.1 National Resource for Computers inLife Science Education

Mail Stop RC-70University of WashingtonSeattle, WA 98195(206) 548-6244

N.2 Neosoft, Inc.CBS Interactive LearningOne Fawcett PlaceGreenwich, CT 06836(800) 227-2574

N.3 New Jersey Medical SchoolJoseph Boyle, MD100 Bergen StreetNewark, NJ 07103(201) 456-4464

0.1 Oakleaf SystemsP.O. Box 472Decorah, IA 52101(319) 382.4320

R.1 Randall, Dr. JamesDepartment of PhysiologyMyers HallIndiana UniversityBloomington, IN 47405(812) 335-1574

R.2 Rush Medical CollegeDrs. Joel Michael and Alan RovickDepartment of Physiology1750 West Harrison StreetChicago, IL 60612(312) 942-6426(312) 942-6567

S.1 Scott, Foresmtn and Company1900 East Lake Ave.Glenview, IL 60025(312) 729-3000

S.2 Simpac Educational Systems1105 North Main St.Suite 11CGainesville, FL 32601(904) 376-2049

S.3 Sliwa Enterprises, Inc.2360-J George Washington HwyYorktown, VA 23666(804) 898-8386

11.1 University of Texas System CancerCenter

MDAH, Box 66723 BertncrHouston, TX 77030(713) 792-2581

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AIMS AND SCOPE The goal of Computers in Life Science Education is to provide a means of communicationamong life science educators who anticipate or are currently employing the computer as aneducational tool. The range of content includes, but is not limited to, articles focusing encomputer applications and their underlying philosophy, reports on facult4;:tudentexperiences with computers in teaching environments, and software/hardware reviews inboth basic science and clinical education settings.

INVITATION TO CONTRIBUTORS Articles consistent with the goals of Computers in Life Science Education are invited forpossible publication in the newsletter.




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(II) 1988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENCE EDUCATION 0742-3233!8840.00 + 2.00

41

VOLUME 5 NUMBER 6, JUNE 1988

HAROLD I. MGDELLDepartment of RadiologyUniversity of WashingtcoSeattle, Washington






TERRY M. MIKTIENGraduate School of Biomedical SciencesUniversity of Texas Health Science CenterSan Antonio, Texas



RICHARD STULLCollege of PharmacyUniversity of ArkansasLittle Rock, Manus





NRCLSE

CLSEE3 5(6)41-48, 1988 ISSN 0742-3233

11111111111111111111111111111

CONTENTS

AN OVERVIEW OF EXPERT SYSTEMS 41Monica C. Nagy

WHERE'S THE SOFTWARE? 45

KEEPING ABREAST OF THE LITERATURE 47

AN OVERVIEW OF EXPERTSYSTEMSMonica C. NagyDivision of nstructional Development, University of TexasHealth Science Centerat San Antonio, San Antonio, Texas

Expert systems is a branch of artificialintelligence that has close ties to thelife sciences. One of the first successfulexpert systems was developed by ateam of researchers headed by EdwardShortliffe at Stanford University. Thesystem, known as Mycin, gave adviceon the diagnosis and treatment of infec-tious diseases and could perform aswell as human experts or even out per-form human experts in the field? Arecent bibliographic search revealedthat twenty-four articles on expert sys-tems have been published by health sci-ence journals in the last three years.This paper will examine the character-istics of an expert system, explain itsfunctioning parts, and give a working

example of an actual expert systemsdevelopment project.

An expert system is a computer pro-gram that p, els the actions of a hu-man expert. consultant. The best way tounderstand an expert system is to ex-amine the characteristics of a humanexpert consultant. Consider the follow-ing scenario.

Jane is the office manager of a smallcompany. Being a high prestige orga-nization, the appearance of the clientwaiting area is of the utmost impor-tance. Several months ago, the com-pany invested a large sum of money inindoor plants for the client waitingarea. The plants gave the waiting areaa friendly and relaxed atmosphere that


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42 COMPUTERS IN LIFE SCIENCE EDUCATION, VOLUME 5, NUMBER 6, JUNE 1988

many clients have since commented on.In the last week, however, the plantshave started to look limp. Some ofthem have developed yellow spots ontheir leaves. Since Jane's bachelordegree in business administration didnot prepare her for dealing with sickplants, she decides to hire an indoorplant consultant.

Jane's first step in hiring the plantconsultant is to draw up a list of citeria.She decides that the plant consultantmust have the following five qualities:

KNOWLEDGEThe consultant must be very know-ledgeable about the problem at hand.Someone with several years of expe-rience would be preferred. This per-son does not need to have a Ph. D. inbotany, but they do need to know agood deal about the care of indoorplants.

RELIABILITYThe consultant should be reliable andcapable of judging whether he or sheis able to give advice on the problem.Far too often has Jane been stung byhigh-priced consultants who gave thewrong answer or would not admit thatthey could not solve the problem.

ABILITY TO EXPLAIN WHYThe consultant should also be able toexplain why certain factors are impor-tant. The company cannot afford tohire a consultant every time the plantsget sick. The consultant, theref-re,should be able to explain whawrong and give advice on how to pre-vent the problem from happeningagain.

ABILITY TO EXPLAIN HOWMost of all, the consultant should beable to explain how he or she arrivedat the conclusions and be able to justi-fy the results. This provides a backupin case the results are unusual or donot seem logical.

FRIENDLINESSFinally, an ideal consultant should befriendly and easy to work with.Although this criteria is often hard tomeet, a friendly consultant makes for

a much more pleasant experience andhelps encourage further consultationsin the future.

KEY FEATURES OF AN EXPERTSYSTEM PROGRAMLet's examine each of Jane's five cri-teria for an expert human consultantand see how it applies to an expert sys-tem program. Jane's first requirementwas that the consultant be knowledge-able and experienced in the problemarea. An expert system programachieves this by containing all the rulesand facts about a certain subject area.This collection of rules and facts iscalled the knowledge base.

Jane's second criterion, reliability, isanother crucial factor in an expert sys-tem. An expert system program, justlike its human counterpart, would be oflittle use if it were not reliable. To en-sure reliability, expert system programshave a very narrow scope of expertise.At the present time, you would be hardpressed to find an expert system thatcould simulate the actions and experi-ence of Ph.D. level botanist. Youcould, however, find one that gave ad-vice on common houseplants. A col-league, Janise Richards, is developingone that diagnoses common houseplantailments. When an expert system cangive advice, the advice is usually interms of certainty factors. A certaintyfactor is the degree to which an expertsystem thinks its advice is correct forthat particular case. The certainty fac-tors are reported either as percentages:"50% certainty of outcome #1," or asrange: "On a scale of one to ten, yourchances of #1 are 5."

Jane's next two criteria are the mostimport characteristics of an expert sys-tem program. The ability to answerhow and why are, in my opinion, thetwo features that distinguish expert sys-tems from other advice-giving pro-grams. This ability also makes expertsystem programs a good learning tool.Rather than being simply a passivedevice, an expert system program canshow you the logic behind its questionsand its decisions.

Jane's final criterion, that the expertconsultant be friendly and easy to workwith, also applies to expert system pro-

grams. Most people would be reluctantto reuse any computer program withwhich they had a great deal of difficul-ty. For this reason, expert systemsshould strive to be user friendly.

To summarize, the key features of anexpert system program are very similarto those of a good human expert con-sultant. An expert system contains alarge amount of knowledge about aspecific subject area. In order to ensurereliability, it tells the user when it canand cannot give advice. When it cangive advice, it often uses certainty fac-tors to indicate its degree of confidencein that advice. Finally expert systemshelp their users learn about the subjectarea by explaining their reasoning andtheir advice. Ideally, they. should befriendly and easy to use.

COMPONENTS OF AN EXPERTSYSTEMIn order to acheive these goals, an ex-pert system is constructed of threeparts, the rule base, the database andthe inference engine. The rule baseholds all the expert information. It typ-ically stores this information in theform of if-then rules. For example, arule used in a plant diagnosis expertsystem might read:

If

andand

then

white, wooly spots appear onstem, leaves, or base of stem

leaves develop sticky patchesgrowth is stunted

diagnosis is mealy bugs

This type of if-then rule is also knownas production rule.

The next part of an expert system iscalled the database. The database holdsall the facts about the specific case.These facts are supplied by the user. Inthe case of Jane and her sick plants, thefacts might be "plant appears to belimp" and "yellow patches on leaves."The database changes with each newcase. The database and the rule basetogether are often termed the know-ledge base of the expert system becausethese are the two parts that store theknowledge.

The final, and most important part is

©1988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENCE EDUCATION 0742-3723/B8/S0.00 + 2.00

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COMPUTERS IN LIFE SCIENCE EDUCATION, VOLUME 5, NUMBER 6, JUNE 1988 43

the inference engine. The inference en-gine is the driver of the expert system.It is where all the actual "thinking"takes place. It uses the rules in the rulebase and facts in the database to decideon the advice to give.

BUILDING AN EXPERT SYSTEMExpert systems are easiest to buildwhen the subject area is narrow and therules are straight-forward and non-conflicting. For example, an expertsystem could be written to give adviceon simple operations on a computersuch as changing the default disk driveor displaying a directory.' The rules forperforming these operations are usuallylisted in the computer's guide to opera-tions. When the rules have alreadybeen written down, as in the case of amanual or quick reference guide, thecreation of an expert system is mademuch easier. Many excellent expertsystems have been created by first in-terviewing a number of human expertsand then translating their expertise intorules. This process can be very timeconsuming and can also produce con-flicting rules.

When there is no general consensuson the ruleS, or there exists a multitudeof special exceptions, building an ex-pert system becomes difficult. In col-lege, I once gave a presentation on ex-pert systems to a group of my fellowcomputer science majors. A short ex-ample program on how to identify ascience fiction movie seemed to be ap-propriate since science fiction is of in-terest to most aspiring computer scien-tists. It quir'i became apparent thatscience fiction movies were difficult toclassify. As soon as one set of ruleswas finished, a conflicting examplewould arise. Almost everyone consul-ted had a different definition of a sci-ence fiction movie. Few could agreeon even ten common factors. Howwould you classify The Twilight Zone ?What is the dividing line between Sci-ence fiction and fantasy? Everyoneagreed that a futuristic setting would bea good indicator. How far in the futurethe movie would have to be set couldnot be agreed upon. The presence ofnew technology was another factorcommonly agreed upon. No consensus

could be reached on how new the tech-nology would have to be. Hater cameto find out that this debate has beengoing on for some time and shows nosigns of being settled any time soon. Ageneral consensus on the rules is neces-sary for building an expert system.

Let's see how all this informationapplies to an actual expert system de-velopment project. For the past year, Ihave worked on an expert system withErnst Scheib, DMD, at the DentalSchool of the University of TexasHealth Science Center at San Antonio.Our goal was to design and implementan expert system in the area of endo-dontics, to be used by second year den-tal students. The system was to be usedfor practice in the student lab. Endo-dontics is the area of dentistry thatdeals with the nerves and blood vesselson the inside of the tooth. Our expertsystem, known as "The Hurting ToothAdvisor," deals with diagnosing pain ina single suspect tooth. It does not dealwith pain from other teeth or paincaused by problems outside of themouth. Dr. Scheib felt that endodon-tics, as compared to other areas of den-tistry, lent itself most to an expert sys-tem because of the small number ofpossible diagnoses. Also, the rules for

diagnosis in endodontics are morestraight-forward and less conflicting.

We began with a matrix that listedsigns, symptoms and test outcomesacross the top and possible diagnosesalong one side. We then narrowed thesigns, symptoms and tests down tothree categories, description of thepain, clinical findings, and radiographicfindings. We used the information inthe matrix to write the rules. An expertsystem is much easier to create if therules have already been formulated.

PROGRAM EXAMPLEWhen it came time to transfer the rulesto the computer, we chose to use theEXSYS expert system generator (EX-SYS Inc., P.O. Box 75158, Station 14,Albuquerque, NM 87194). Generatorprograms greatly ease and acceleratethe creation of an expert system. Theytake care of all the details. They usual-ly provide the inference engine and afacility for displaying questions to theend user. They also allow the end userto ask why a question is being askedand, if they so desire, to see the rulesthat were used. Best of all, most gen-erator programs are designed to be usedby non-programmers.

Besides the features mentioned above,

Add rule <A> or <ENTER>, Edit rule <E>, Delete rule <D>, Move rule <M>,Print <P>, Store/exit <S>, Run <R>, Options <0>, DOS <Ctrl-X>, Help <H>

FIGURE 1. The main edi bar screen.

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4 4


EXSYS has a specialized editor for en-tering rules. This means that it promptsthe person editing the rule for each ofthe rule's components (see Figure 1).Once the rules are entered, they can betested right away by pressing "R" forrun. The atecedent, or "if' part, of eachrule is translated into a question for thestudent to answer. The expert systemuses the rules themselves and theanswers that the student has given thusfar to determine which question will beasked next. For instance, in Figure 2, ifthe student answers that the toothshows signs of caries, the next questionwould ask how extensive the carieswere. If the student did not indicatethat caries could be seen, the expertsystem would go on to the next cate-gory of questions. The student wouldnot see the extra question on caries.

While using the expert system, a stu-dent can ask why a question is beingasked by entering "WHY" as the re-sponse to a question. The student alsohas the option of seeing the rules asthey are used. Once the student hasanswered all the questions, the possiblediagnosis and treatment plan are dis-played. At this point, the student caneither print the advice, change some of

THE SUSPECT TOOTH SHOWS SIGNS OFI. CARIES2. PREVIOUS RESTORATIONS3. WEAR ON THE OCCLUSAL ENAMEL SURFACE4. CERVICAL ABRASION

Enter numbcr(s) of value(s), WHY for information on the rule,<?> for more details, QUIT to save data entered or <H> for help.

FIGURE 2. A question screen. At this point the student could enter the number correspond-ing to a selected choice, the word "WHY" tosee the current rule, a "?" for more details,"QUIT" to save the information entered so far or "H" for help on using the expert system.

their answers, and run the system again,or start over from the beginning (seeFigure 3).

The Hurting Tooth Advisor is now ina prototype stage, meaning that it gives

Values based on 011 system

1. CAN GIVE ADVICE2. INFERRED TREATMENT IS APPLY SODIUM FLUORIDE3. INFERRED DIAGNOSIS IS CERVICAL ABRASION

VALUE

1

1

All choices <A>, only if value>1 <G>, Prin: <P>, Change z -id rerun <C>, rules used <linenumber, Quit/save <Q>, Help <H>, Done <D>:

FIGURE 3. The conclusions are displayed in terms of certainty factors. In this case, thecertainty factor will either be zero, for false, or one, for true.

accurate results only for a few chosencases. We hope to develop this into afully operational system.

In conclusion, an expert system is acomputer program that simulates thehuman capabilities of knowledge, relia-bility, the ability to explain the impor-tance of key factors, the ability to ex-plain how a conclusion is reached, andthe ability to be friendly and easy touse. Expert systems attain these abili-ties by organizing information into arule base and a database. They alsocontain an inference engine that drivesthe whole process. Finally, an expertsystem is easiest to build in an area thathas well codified rules and guidelinesthat are straight-forward and non-conflicting.

REFERENCES1. Nagy, T, D Gault, and M Nagy. Build-

ing Your First Expert System. Aston-Tate, Culver City, CA, 1985.

2. Winston, PH and KA Predergast (eds).The AI Business: The Commercial Usesof Artificial Intelligence. MIT Press,Cambridge, MA, 1984.

©1988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENCE EDUCATION 0742-3233/88/80.00 + ZOO


WHERE'S THE SOFTWARE?In the past, we have published lists oflife science software sources and pro-grams or program areas availablethrough them. The following list ispresented as the latest in a continuingeffort to make colleagues aware of po-tential resources. As in the past, noattempt has been made by NRCLSE toreview these materials.

This month's listings continue lastmonth's and are arranged by contentarea. Each item includes a vendor coderelating the software to the vendors ap-pearing at the end of the software lists.

If you have found specific softwarehelpful in your teaching efforts, pleaseshare your good fortune by letting usknow about the program(s) and sup-plier(s) so that we can make this infor-mation available through futureWhere's the Software lists. Send per-tinent information to Dr. HaroldModell, NRCLSE, Mail Stop RC-70,University of Washington, Seattle, WA98195 or send us a note on BITnet.Our BITnet address is MODELL@UWALOCKE.

PHYSIOLOGYCONCEPTS IN THERMOGRAPHY

Tutorial covering basic DC concepts,peripheral vascular physiology, detectingskin temperature, amplifiers, and processingDC signals. Program for Apple IIequipment. B.2EXERCISE EXPERIMENTS

Part of 10 program package Experimentsin Human Physiology. The effect ofexercise and physical condition on heartrate, breathing rate, and skin temperature isinvestigated. Program available for Apple11 equipment. H.1GAS DIFFUSION IN THE LUNG

Simulation of oxygen and CO2 transferbetween alveolar air and blood. Programavailable for IBM-PC compatibleequipment. I.1HEART RATE

Part of 10 program package Experimentsin Humar.Physiology. Light and lightsensor for measuring and recording heartrate. Program available for Apple IIequipment. H.1HOMEOSTASIS- THERMOREGULATION

Part of 10 program package Experimentsin Human Physiology. Students investigatethe body's ability to maintain a constant

internal temperature by subjecting a volun-teer to mild temperature excursion whilerecording and displaying skin and bodytemperature. Program available for AppleII equipment. H.1HUMAN BODY-STRUCTURE ANDFUNCTION

Simulation covering joint movement,movement of food through digestive sys-tem, and enzyme activity. Program avail-able for Apple II equipment. S.1MODEL NEURON

Simulation of the behavior of an isolatedneuron. Program available for Macintoshequipment. K.1MUSCLE MECHANICS: A COMPUTER-SIMULATED EXPERIMENT

Simulated experiment that permits theuser to determine either the length-tensionor the force-velocity relationship of askeletal muscle. Program available forIBM-PC compatible equipment. R.2NERVOUS SYSTEM

Tutorial covering nerves, reflexes, andchemical transfer of impulses. Programavailable for Apple II and TRS-80 ModelIII equipment. 3.1PHYSIOLOGICAL DATA SIMULATION

25 simulations covering aspects of physi-ology. Program available for Apple II andIBM-PC compatible equipment. 0.1PROBLEMS IN FLUID COMPARTMENTRE-DISTRIBUTION

Tutorial covering solution of simple prob-lems of fluid compartment changes in theface of perturbations. Progam available forIBM-PC compatible equipment. R.2PSYCHOLOGICAL STRESS-LIEDETECTOR

Pan of 10 program package Experimentsin Human Physiology. The physiologicalresponse to the stress of a frustrating andabusive quiz is measured. Programavailable for Apple II equipment. H.1PULMONARY MECHANICS

Tutorial and simulation dealing with pul-monary mechanics. Available for IBM-PCcompatible equipment. N3RESPIRATION RATE

Part of 10 program package Experimentsin Human Physiology. A napping subject ismonitored for heart and breathing rate.Results are compared to the data acquiredwhen the subject is awake. Programavailable for Apple II equipment. H.1RESPONSE-TIME

Part of 10 program package Experimentsin Human Physiology. Users measurefinger reaction times with a bright lightstimulus (sensor included). Program

available for Apple II equipment. H.1RESPONSE-TIME INVESTIGATIONS

Part of a 10 program package Experimentsin Human Physiology. The effects on reac-tion times of stimulus type and responselocation are studied. Program available forApple II equipment. H.1RESPSYST, GASEXCH

Simulations dealing with pulmonary gasexchange. Available for IBM-PC compat-ible equipment. N3SIMULATIONS IN PHYSIOLOGY - THERESPIRATORY SYSTEM

Series of 12 simulations dealing withrespiratory mechanics, gas exchange,chemoregulation and acid-base balance.Program available for Apple II, IBM-PCcompatible, and Macintosh equipment. N.1SKELETAL MUSCLE ANATOMY/PHYSIOLOGY

Tutorial covering three muscle categories,skeletal muscle microstructure, sliding fila-ment theory, motor units, and lever systems.Program for Apple II equipment. B.2SKELETAL MUSCLE MECHANICS

Set of six simulations dealing with musclephysiology. Program available for IBM-PCcompatible equipment. 1.1SKILLS IN ELECTROMYOGRAPHY

Tutorial covering skin preparation, reduc-ing EMG artifact, testing a myograph's op-eration, electrode location, and preventingshock hazards. Program for Apple IIequipment. B.2SKIN TEMPERATURE

Part of a 10 program package Experimentsin Human Physiology. Temperature probe(included) senses body and skin tempera-tures. Program available for Apple IIequipment. H.1

POPULATION DYNAMICSCOEXIST:Population Dynamics

Simulation of the growth of two popula-tions either independently or in competitionfor the same limited resources. Programavailable for Apple II equipment. C.4ISLAND BIOGEOGRAPHY

Three simulations of island communitiesdealing with the relationship between islandarea and number of species, colonization ofa netv island, and island immigration andextinction. Program available for Apple IIequipment. C.4LIMITS

Simulation of the effects of growth onworld population, pollution, food supply,industrial output, and natural resources.Program available for Apple II, PET/CBMand TRS-80 Model III equipment. C.3


461


MARK & RECAPTURESimulation of mark and recapture experi-

ments to explore three models for estimat-ing population sizes. Program available forApple II equipment. C.4POP

Simulation of three growth models (expo-nential, logistical, and logistical with lowdensity). Program available for Apple II,PET/CBM, and TRS-80 Model III equip-ment. C3POPGRO-POPULATION GROWTHSIMULATION

Simulation of unlimited growth (J-curve),limited growth (S-curve) and limited growthwith response lag time (S-curve withoscillations) models of population growth.Program available for Apple II, TRS-80Models I and III, IBM -PC, and Commodore64/128 equipment. D.2POPULATION FLUCTUATIONS

Tutorial covering factors influencing pop-ulation growth. Program available for Ap-ple II, TRS-80 Models I and III equipment.E2POPULATION GROWTH

Simulation dealing with exponential anddensity-dependent gowth. Program forApple II equipment. C.2POPULATION GROWTH

Simulation of population growth. Theparkage compares and contrasts the geo-metric or exponential growth model withthe logistic or Verhulst-Pearl growth model.Program available for Apple II equipment.C.4POPULATON SIZES

Simulation dealing with a dynamic popu-lation. Program for Apple II equipment.C.2

SUBSTA,s'CE ABUSEDRINKING AND NOT DRINKING

Tutorial designed to augment strategiesfor the prevention of substance abuse.Includes facts about drinking and the effectsof alcohol. Program available for Apple IIequipment. K.1INTRODUCTION TO PSYCHOACTIVEDRUGS

Tutorial designed to augment strategiesfor the prevention of psychoactive drugabuse. Program available for Apple IIequipment. K.1KEEP OFF THE GRASS

Tutorial designed to augment strategiesfor the prevention of marijuana abuse. Pro-gram available for Apple II equipment. K.1SIX CLASSES OF PSYCHOACTIVEDRUGS

Tutorial designed to augment strategiesfor the prevention of psychoactive drugabuse. Program available for Apple IIequipment. K.1

SUBSTANCE ABUSE DATA BASEDatabase containing contact information

on substance abuse organizations. Programavailable for Apple II equipment. K.1

ZOOLOGYZOOLOGY I

Tutorial covering the general charactistics,structures, and functions that define themajor invertebrate phyla. Program avail-able for Apple II and IBM -PC compatibleequipment. S.3ZOOLOGY II

Tutorial covering physiology in the Phy-lum Chordate. Program available for AppleII and IBM-PC compatible equipment. S3

MISCELLANEOUSBAFFLES, BAFFLES II

Game to help students develop deductivereasoning and problem solving skills.Program available for Apple II (BAFFLES)and IBM-PC compatible (BAFFLES II)equipment. C.4BALANCE-PREDATOR-PREYSIMULATION

Simulation of the effects of food supply,carrying capacity, environmental condi-tions, and external pressures or predator/prey relationships. Program available forApple II, TRS-80 Models I and III, IBM-PC, and Commodore 64/128 equipment.D.2BLANCHAER CLINICAL CASE STUDIES

Eight simulations of clinical syndromes.Program available for Apple II equipment.B.1CLASSIFY-CLASSIFICATION KEYPROGRAM

Presents an unclassified set of characteris-tics and labels for classification at variouslevels. Program available for Apple II,TRS-80 Model III, IBM-PC, and Commo-dore 64/128 equipment. D.2GRADE KEEPER - PC

Grade book manager that handles classesup to 300 students, lip to 25 grades perstudent. Program available for IBM-PCcompatible equipment. 0.1GRADE3If

Program for the analysis of a large set ofgrades. Program available for IBM -PCcompatible equipment. I.1LABPLOT

Allows the Apple II with any A/D con-verter card to be used as a multipen chartrecorder or as an X/Y plotter. B.1LIFE TABLES AND THE LESLIE MATRIX

Tutorial-simulation dealing with the basiclife table and Leslie Matrix. Programavailable for Apple II equipment. C.4MALARIA

Simulation of the effects of various typesof malaria epidemic controls. Program

available for Apple it, PET/CBM, and TRS-80 Model Hi equipment. C.3MULTI-Q

A general purpose question creation andpresentation system. Program available forApple II and IBM-PC compatible equip-ment. B.1PREDATION

Simulation of predator-prey interactions.Program available for Apple II equipment.C.4PREDATION EQUILIBRIA

Simulations of equilibrium models ofpredator-prey interaction. Program avail-able for Apple II equipment. C.4"Q" EDUCATIONAL AUTHORINGSYSTEM

Authoring system for tutorial and assess-ment material. Allows incorporation ofgraphics and videodisc material. Programavailable for IBM-PC compatible equip-ment. B.1RATS

Simulation of rat control in city or apart-ment by sanitation and various poisons.Program for Apple II, PET/CBM, and TRS-80 Model III equipment. C.3STERL

S;:nulation exploring effectiveness of pestcontrol methods. Program available forApple II, PET/CBM, and TRS-80 Model IIIequipment. C.3TRIBBLES,TRIBBLES Revisited

Simulation to introduce students to thescientific method. Programs available forApple II (Tribbles) and IBM-PC compatible(Tribbles revisited) equipment. C.4

VENDORS

A.1 Academic HallmarksP.O. Box 998Durango, CO 81301(303) 247-8738

B.1 BIOSOFT22 Hills RoadCambridge CB2 UP, United Kingdom

P.O. Box 580Milltown, NJ 08850

B.2 Biosource Software2105 S. Franklin, Suite BKirksville, MO 63501(816) 665-3678

C.1 Classroom Consortia Media, Inc.57 Bay StreetStaten Island, NY 10301(800) 237-1113(800) 522-2210

C.2 COMpressP.O. Box 102Wentwonh, NH 03282(603) 764-5831

1988 BY NATIONAL RESOURCE FOR COMPUTERS IN UFE SCIENCE EDUCATION 0742-3233/88/S0.00 + 2.00


C3 Compuware15 Center RoadRandolph, NJ 07869(201) 366.8540

C.4 CONDUITThe University of IowaOakdale CampusIowa City, IA 52242(319) 335.4100

D.1 Datatech Software Systems19312 East Eldorado DriveAurora, CO 90013

D.2 Diversified Education Enterprises725 Main StreetLafayette, IN 47901(317) 742-2690

D3 DYNACOMP, Inc.1064 Gravel RoadWebster, NY 14580(716) 671-6160(800) 828-6772

E.1 Educational Activities, Inc.P.O. Box 392Freeport, NY 11520(800) 645-3739(516) 2234666

E.2 Educational Materials andEquipment Co.

P.O. Box 17Pelham, NY 10803(914) 576-1121

E3 EduTech, Inc.303 Lamatine StreetJamaica Plain, MA 02130(617) 524-1774

1-1.1 FIRM Software175 Tompkins AvenuePleasantville, NY 10570(914) 769-7496(800) 431-2050

LI Indiana University School of MedicineDepartment of Physiology and Biophysics635 Barnhill DriveIndianapolis, IN 46223

J.1 J & S Software14 Vanderventer AvenuePon Washington, NY 11050(516) 944-9304

K.1 Kinko's Service Corporatiow4141 State StreetSanta Barabara, CA 93110(800) 235.6919in CA (800) 292.6640outside USA (800) 967-0192

L.1 Life Scierce Associates1 Fenimore ReadBayport, NY 11705(516) 472-2111

N.1 National Resource for Computers inLife Science EducationMail Stop RC-70University of WashingtonSeattle, WA 98195(206) 548-6244

N.2 Neosoft, Inc.CBS Interactive LeamingOne Fawcett PlaceGreenwich, CT 06836(800) 227-2574

N3 New Jersey Medical SchoolJoseph Boyle, MD100 Bergen StreetNewark, NJ 07103(201) 456-4464

0.1 Oakleaf SystemsP.O. Box 472Decorah, IA 52101(319) 382-4320

R.1 Randall, Dr. JamesDepartment of PhysiologyMyers HallIndiana UniversityBloomington, IN 47405(812) 335-1574

R.2 Rush Medical CollegeDrs. Joel Michael and Alan RovickDepartment of Physiology1750 West Harrison StreetChicago, IL 60612(312) 942-6426(312) 942-6567

SI Scott, Foresman and Company1900 East Lake Ave.Glenview, IL 60025(312) 729-3000

S.2 Simpac Educational Systems1105 North Main St.Suite IICGainesville, FL 32601(904) 376-2049

S3 Siiwa Enterprises, Inc.23604 George Washington HwyYorktown, VA 23666(804) 898-8386

U.1 University of Texas System CancerCenterMDAH, Box 66723 BertnerHouston, TX 77030(713) 792-2581

KEEPING ABREAST OF THELITERATUREThe following citations are presented aspart of a quarterly feature in CLSE de-signed to help readers become aware ofcurrent literature pertinent to computerapplications in life science education.

Allan DM et al: Issues in the adoptionof a new educational technology.Comput Methods Programs Biomed25(2):103-109, 1987.

Allen BS et al: Training interactivevideo designers. J InstructionalDevelopment 9(2):19 -28, 1986.

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0 1988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENCE EDUCATION 0742- 3233/88/S0.00+ 2.00

4

VOLUME 5 NUMBER 7, JULY 1988

COMPUTERSIN LIFESCIENCE-EDUCATION

HAROLD I. MODELLDepartment of RadiologyUniversity of WashingtonScaule, Washington

MARCEL BLANCHAERDepartment of BiochanistriFaculty of MedicineUniversity of ManitobaWinnipeg, Manitoba

THEODORE J. CIOVELLOGraduate Studies and Reseed-California State University, 1.01 AngelesLes Angeles, California


TSEGAI'E HABTEMARIAMSchool of Veterinary MedicineTuskcgce UniversityTuskegee Alabama

DONNA LARSONSchool of NursingGrand Vac,/ Star.: Coll a,t.Allendale, 10' rjgan

TERRY M. MIKITENGraduate school of Biomedical 'icier:asUniversity of Texas Health Science CenterSaa Antonio, Texas

JAMES E. RA/CALLDvanment of Phyr;ologyIndiana Uni. ersityBloomington, Indians


RICHARD STULLCollege of PharmacyUniversity of ArkansasLittle Rock, Liransas

JAMES W. WOODSLister Hill National Centerfor Biomedical Communications



GLEN YOSHIDADepartment of Life SciencesLos Angeles Southwest CollegeLoa Angeles, California

NRCLSE

CLSEE3 5(7) 49-56, 1988 ISSN 0742-3233

111111111111111111111111111111111

CONTENTS

A GRAPHIC COMPUTER LANGUAGE FORPHYSIOLOGY SIMULATIONSJ.W. Kiel and A.P. Shepherd

11=M11. 17MEIN IIMMEA

49

A GRAPHIC COMPUTERLANGUAGE FOR PHYSIOLOGYSIMULATIONS

LW. Kiel and A.P. ShepherdDep. intent of Physiology, University ofTexas Health Science Center, San Antonio, Texas

.;nd other life scientistscan use inathematical models profitablyto create and test new scientific theo-ries as well as to demonstrate to theirstudents th' aior of complex bio-logical sys I .fortunately, creat-ing such tut ofte-,1 requires morecomputer programming expertise ormore time and effort than the investsgattsr can afford to invest in n modelwhether it is a research tool or a didac-tic device. Therefore, we were in-trigued by Lai view', a graphic pro-

The graphic programming language is actuallycalled G,but it is better known by the trade nameLabView, an acronym for laboratory virtual in-strument engineering workbench. The LabViewpackage consists of four 3.5 inch disks and atwo-volume r .nuaL It is available from Nation-al Insuume.as, 12109 Technology Blvd., Austin,Texas, 78727-911% and it runs on a 1Mb MacPlus, SE, or E. An external floppy or barn diskis required.

gramming language that recently be-came available for the Macintosh com-pwr.

Because physiologists generally thinkof organ systems in terms or block &-grains and feedback loops, we thoughtperhaps a graphic prt -aiming lan-guage would make meting amathematical model easier, less time-consuming, and more intuitive thanconventional computer languages.

Therefore, the purpose of this com-munication is to report our experienceusing LabView to construct mathemat-ical models of physiological systems.

Because LabView was originallydeveloped to control laboratory instru-ments and to collect and analyze data, aLabView program can be thought ofmetaphorically as a "virtual instru-ment." A virtual instrument consists ofa front panel and an executable blockdiagram. In one window on the com-

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COMPUTERS IN LIFE SCIENCE EDUCATION, VOLUME 5, NUMBER 7, JULY 1988

puter screen, the front panel displaysthe various input and output controlsavailable to the user or programmer:graphic representations of switches,dials, knobs, digital or analog meters,strip-charts, etc. The appearance ofthese controls or display deviceschanges to illustrate the data going intoor out of the virtual instrument.

A second window contains the blockdiagram that constitutes the programthat the computer executes. In theblock diagram, numeric variables, dis-play devices, program control struc-tures such as For-Next loops, and arith-metic operations are nodes representedgraphically by icons. The programmeruses various menus to obtain the de-sired icons and then places them in theblock diagram. "Wires" connecting theicons define the path of data flow fromone node to the next. The executionenvironment is inherently parallel anddata-driven, (ie, each icon or node exe-cutes its particular function when allnecessary input data are available toit).5 The output of that particular nodeis then passed on to the next. Theblock diagram of one virtual instrumentcan even contain other virtual instru-ments within it. Thus, previously pro-grammed routines can easily be incor-porated into a more elaborate model.According to a recent report, "the in-tended user base for LabView includesengineers and scientists with no pro-gramming experience or limited expe-rience with a simple language likeBASIC." 5 Therefore, being only mod-erately proficient in BASIC, we felt ap-propriately qualified to evaluate Lab-View as a programming language forphysiological simulations. To do so,we programmed two well -kncwn car-diovascular models in Lab View. In therest of this report, we describe the pro-grams themselves, our experience increating them, and our perceptions ofVie strengths and weaknesses of Lab-View as a method for modeling physio-logical systems.

A SIMPLE CARDIOVASCULARMODELThe following model of the r,ardiovas-cular system was originally proposedby Guyton et a12 Rothe later devel-oped it into a teaching tool for courses

ir. cardiovascular physiology. Themodel was designed to simulate theinteraction between the heart and theperipheral vascular system and to dem-onstrate homeostatic responses to avariety of perturbations to the cardio-vascular system (eg, exercise, cardiacfailure, and hemorrhage).

The model consists of three compart-ments: a single-chambered heart, anartery, and a vein. Under steady-stateconditions, each compartment containsa specific volume of blood. The pres-sure in each compartment is deter-mined by the compartment's compli-ance and its blood volume. Blood flowbetween compartments is a function ofthe pressure gradients and resistancesbetween compartments. To simulatethe Frank-Starling mechanism crudely,cardiac output is computed simply as alinear function of heart volume. Al-though there are no "reflexes" per se,the appropriate cardiovascular effectors(ie, arterial resistance and compliance,venous resistance and compliance, andcardiac contractility) c °- be modified

Art.A Uen.A

.05

.20

2 'IS.10

11 to .osi01 0

Art.0 Ven.0

.20 4

.15 3.10 205 1

0 0

Art.0 Ven.0

114.001

Art.P Ven.P

18.00

On/Off

.92

.04

by the user. In addition, there is also aprovision for simulating hemorrhage ortransfusion by increasing or decreasingthe total blood volume.

Figure 1 shows the "front panel" ofthe cardiovascular model as it appearson the Macintosh screen. The slideswitches permit the user to change theresistances (Art.R and Ven.R) andcompliances (Art.0 and Ven.C) of thetwo vascular compartments, cardiaccontractility (Ctrt), and the total bloodvolume (AVol). The two strip-chartscontinuously display the cardiac outputand arterial pressure. The digital in-dicators for each compartment showthe current numerical values for theblood volumes (Art.V, Ven.V, andHrt.V) and pressures (Art.P, Ven.P, andHrt.P) as well as the total blood volume(Tot.V). In Figure 1, the input and out-put values shown on the slide switchesand other indicators are the initial de-fault values specified by Rothe.4

Figure 2 shows the block diagram forthe simple cardiovascular model. Allof the actual calculations are performed

Simple Cardiovascular Model Panel

4.00

100

Ctrt

100

100- _10

0 20

01300 20

200 10

AUol.0

Hrt.0

2.50 1

Hrt.P

3.00

Tot.0

20.50

Cardiac Output (ell/min/kg)

_1 1 j MULL'

11111111111111111

200

150

100

SO

0

FIGURE 1. Front panel of a simple cardiovascular model. A window on the computersf.:teen shows the four types of control and display devices used in this model. Slide switchesselect values for cardiac contractility (Ctrt), the rate of hemorrhage or infusion (Vol), andarterial and venous resistances (Art.R and Ven.R) and compliances (Art.0 and Ven.C).Digital indicators show blood volumes in the three compartments, and strip-chart simulatorsprovide a continuous record of cardiac output and arterial pressure. A binary on-off switchstops and starts program execution.

©1988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENCE EDUCATION 0742-3233A8/50.00 + 2.00

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COMPUTERS IN LIFE SCIENCE EDUCATION, VOLUME 5, NUMBER 7, JULY 1988 51

within a While-Wend Loop activatedby an On/Off switch located on thefront panel. In the diagram, the On/Offswitch is "wired" to the "recirculationterminal" (counterclockwise arrow) atthe bottom right-hand corner. TheWhile-Wend Loop is used to providecontinuous operation and to store thecompartmental volumes for use in thenext iteration. These values are storedfrom one iteration to the next by thethree shift-registers (boxed arrows)located on the left and right borders ofthe While-Wend Loop structure. Thethree numeric constants wired to theshift-registers on the left border of theWhile-Wend Loop initialize the shift-registers and provide the starting valuesof the blood volumes in the three com-partments.

Within the While-Wend Loop, theprogram proceeds from left to right.The first step in the program is to cal-culate the pressures in the three com-partments by dividing the volumesspecified in the three shift-registers bythe corresponding compliance valuesselected on the front panel. These cal-culations are performed by three divi-sion icons. For example, to calculatethe pressure in the arterial compart-ment, the arterial shift-register is wiredto the upper terminal (the numerator) ofone division icon, and the arterial com-pliance slide switch control is wired tothe bottom terminal (the denominator).The output terminal of the division icon(the quotient) is wired to both a strip-chart indicator and a numeric indicatorto display the arterial pressure on thefront panel. The same procedure isused to calculate the pressures in thevenous and cardiac compartments.

The next step in the program is to cal-culate the pressure gradients betweenthe arterial and v 'ous compartmentsand between the venous and cardiaccompartments. These pressure differ-ences are calculated by wiring the threedivision output terminals into two sub-traction icons. The pressure differ-ences are then divided by the arterialand venous resistances to determine theblood flow from the arterial to the ve-nous compartment and from the venouscompartment to the heart.

As shown in the lower portion of Fig-ure 2, cardiac output is simply a 'Anew

Simple Cardiovascular Model Diagram

OrterlolPressure

Pet .t1

BEIHrtVol

Ctrt.

100

£V0 .MCordlac

Output

FIGURE 2. Block diagram of a simple cardiovascular model. A Lab View diagram is theprogram that the computer executes. Each icon represents the arithmetic operation denotedby its symbol or corresponds w a switch or display device on the front panel shown in Figure1. "Wires" define the path of data flow. For details of operation, see text.

function of heart volume and is calcu-lated by multiplying the heart volumeby the slope of the relationship betweenheart volume and cardiac output. How-ever, note that the slope of this relation-ship depends on the cardiac contractil-ity (Hrt. Curt) and that cardiac contrac-tility can be changed from the defaultvalve (100%) by using the slide switchon the front panel.

Under steady-state conditions, bloodflow through the three compartmentswill be equal. However, following anyperturbation, a transient imbalance inblood flow between any two compart-ments will redistribute the blood vol-ume. Therefore, the next step in theprogram is to calculate the volumeshifts among the three compartments(ie, cardiac output minus arterial run-off, arterial runoff minus venous return,and venous return minus cardiac out-put). These dirterences between eachcompartmclit's inflow and outflow arethen multiplied by an increment in time(do. The result 0,f this integration isadded to the old volume from the pre-vious iteration (from the shift-registeron the left) to yield the new instanta-neous volume in each compartment.The new instantaneous volume is then

passed to the shift register on the rightand stored until the next iteration. Fi-nally, the slide switch AVol allows theuser to select a rate at which blood vol-ume is gained or lost through trans-fusion or hemorrhage.

A MODEL. OF PULSE PRESSURETo show a slightly more elaboratemodel programmed in LabView and todemonstrate other capabilities of Lab-View not illustrated in the previous ex-ample, we present a model of the arte-rial pressure pulse that was originallypublished by Coleman and Sias' andlater used by Randall for didactic pur-poses? The model was designed todemonstrate the physics of the arterialpressure pulse and to show how theamplitude of the arterial pressure waveis affected by stroke volt:ate, arterialcompliance, and the ventricular ejec-tion rate. The model consists ofa singleventricle and an elastic arterial com-partment. We modified the Coleman-Sias-Randail model so that the ventri-cle receives a variable venous returnand has an adjustable heart rate. Thesetwo factors determine the stroke vol-ume and the rate of ventricular ejectioninto the arterial compartment. The

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52 COMPUTERS IN LIFE SCIENCE EDUCATION, VOLUME 5, NUMBER 7, JULY 1988

instantaneous volume in the arterialcompartment is set by the differencebetween ventricular ejection and theperipheral runoff. The instantaneousvolume and the arterial compliancedetermine the instantaneous pressure.

Figure 3 shows the front pane! of thepulse pressure model. The slide switch-es select values for the venous return(V.R.), heart rate (H.R.), compliance(C.), and total peripheral resistance(T.P.R.). In addition, two numeric con-trols specify values for the fraction ofthe cardiac cycle devoted to systole(systolic fraction) and the integrationinterval (dt). The two strip-charts dis-play the ventricular ejection rate andarterial pressure. The four digital dis-plays show the current values of thestroke volume, systolic and diastolicpressures, and the amplitude of thepressure pulse. The initial values andconstants shown in Figure 3 were takenfrom Randall.'

Figure 4 shows the Mock diagram forthe pressure pulse model. Like the pre-vious model of the cardiovascular sys-tcm, all the calculations for the modelare performed within a While-Wendloop to provide continuous operationand to store current values in shift-

U.R. H.R.

5 60

15 150

10 100

5. 50

0 0

C. T.P.R.

1.0 1.2

3 3

2 2

1

0 0

Stroke Volumel/beat

F401

al

65

Pressure Pulse Model Diagram

1000

SF

L=1 s

U.E. SystolicPressure

dt

VentricularEjection

Pressure On/OffPulse

DiastolicPressure

Peripheral

RunoffCompliance

Rrterial Pressure

ICI

ItI* of

.waiazimizgozzimimmasziA

FIGURE 4. Executable diagram of pulse pressure model. The main diagram contains five"virtual instruments" also programmed by the authors: timer (hourglass icon), ventricularejection (V.E.), integrator (integral sign), error-trapping routine (zero check), and waveanalyzer (connected to pressure pulse display). For details, sec text.

registers for use in the subsequent itera-tion. However, unlike the previousmodel, the pressure pulse model con-tains a number of virtual instrumentsthat perform the more complicated pro-gram operations, such as timing thecardiac cycle and calculating the rate of

Pressure Pulse Model Panel

TiTimMFTIHHIHM-111-,1:111 I_M_MLr.1 I I 1_03_1 I I

11.11.1 I II I O-il HIHHH

II I 11 1111 li H

OEM

systolic dt On/Offfraction

SystolicPressure

1120 1 65DiastolicPressure

PressurePulse

FIGURE 3. Front panel of pulse pressure model. Slide switches select values for venomreturn (V.R.), heart rate (H.R.), arterial compliance (C), and total peripheral resistance(T.P.R.). Constants are the systolic fraction of the. cardiac cycle and the integration interval(dt). Computed variables are shown on simulated stripcharts or digital displays.

ventricular ejection, ciscussed in detailbelow.

As in the previous model, the pro-gram execution proceeds from left toright and starts with the initialization ofthe shift-registers. The complex iconwired to the top shift-register on theleft border is an "array builder." Thetwo numeric constants specifying theinitial systolic (S) and diastolic (D)pressures are wired to the left side ofthe array builder which generates atwo-element array that is passed to theshift-register. Note that in LabView,the appearance of a wire depends onthe type of data it carries (ie, numeric,array, string, or boolean data) and thatthe array wire is thicker than the wireused for numeric data. The other threeshift-registers are initialized by numer-ic constants specifying the starting val-ue for the timer (n) and the default val-ues for arterial volume (V) and pres-sure (P).

Inside the While-Wend loop, usingvalues specified on the front panel forvenous return, heart rate, and systolicfraction of the cardiac cycle, the pro-gram begins by calculating the strokevolume, the period of the complete car-diac cycle, and the duration of systole.The cardiac period and the du...donofsystole as well as the current valuesof n and dt are wired to the timer sym-

C 1988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENCE EDUCATION 0742-3233/88/10.00 + 2.00

to


bolized by the hourglass icon. Thetimer is the virtual instrument respon-sible for the timing of each cardiaccycle and for signaling the beginningand end of systole.

The diagram for the timer is shown inFigure 5. The timer operates by settingn equal to zero at the beginning of eachcardiac cycle and incrementing n byone during each iteration until the car-diac cycle is completed. The numberof iterations required for a cycle is thecardiac period divided by dt. Duringeach iteration, by using the "greaterthan, equal to, or less than" function, nis compared to the calculated numberof iterations per cycle. The output ofthis comparator is boolean (true orfalse) as indicated by the dotted lineconnecting the "less than" terminal tothe case structure selector. So long asn is less than the calculated number ofiterations per cycle, the program exe-cutes the operations specified in theTRUE form of the case structure, and nis incremented by one. At the end of acycle, when n is no longer less than thecalculated number of iterations per cy-cle, the FALSE form of the case struc-ture is executed, and n is reset to zero.The time since the beginning of eachcycle is simply the product of n multi-plied by dt. A second comparator iconis used to compare the elapsed cycletime with the calculated systolic dura-tion and to provide a boolean signal forthe beginning and end of systole.

The instantaneous rate of ventricularejection is calculated by the virtual in-strument labelled V.E. in Figure 4. Theblock diagram for the V.E. virtual in-strument is shown in Figure 6. Froman equation given by Randa11,3ventric-ular ejection (VE) is calculated as thepositive half of a sine wave:

VE=(SV/SD) SIN (I t time/SD)/0.636

Here, SV is the stroke volume, SD isthe systolic duration, and time is thetime elapsed since the beginning ofsystole. The constant, 0.636, simplyconverts the average systolic ejectionrate (SV/SD) to its maximum systolicvalue. In the diagram (Figure 6), thiscalculation is performed within aTRUE-FALSE case structure. Duringsystole, while the boolean signal from

Pressure Pulse Model Timer Diagram

old n itz341

]cycle time

systot lcItz311 systoleduration

FALSE

-1 '1

0

!I '''' ' ''''' <444.4.

'cycle 4Ime

systolicduration systole

new n

INEGI

new n

FIGURE 5. Diagram of the virtual instrument "timer." Shown are contents of timer thatappears as the hourglass icon in Figure 4. From the period of total cardiac cycle and theintegration interval (dt), this instrument calculates the number of iterations per cardiac cycle,computes the time elapsed since the beginning of thecurrent cardiac cycle, and provides aboolean (true-false) signal of whether or nut the heart is in systole. Both the true and falseforms of the case structure are shown.

the timer is true, the calculations spec-ified in the TRUE form of the casestructure yield the instantaneous ven-tricular ejection. During diastole,while the boolean signal from the timeris false, the FALSE form of the casestructure simply generates a zero forventricular ejection The histantaneousvolume in the arteria, compartment isdetermined by the dill 'rence betweenventricular ejection ano peripheral run-off. On the first iteration, perii.Aeralrunoff is calculated by dividing the ini-tial diastolic pressure (65 mmHg) bythe total peripheral resistance set on thefront panel slide switch. At the centerof the main diagram (Figure 4), periph-eral runoff is sub.xacted from ventricu-lar ejection. The difference betweenarterial inflow and outflow is multi-plied by dt within the virtual instrument

symbolized by the homemade integralsign. The result of this integration isthe volume increment or decrementthat is added to the arterial volumefrom the previous iteration.

Although it is impossible foran arte-rial system to have a negative volume,a computer model is under no such re-striction. Indeed, depending on thevalues selected on the front panel, it isquite possible for the present model todevelop negative volumes in the arteri-al compartment or to generate invalidnumbers that require the user to restartthe program (eg, by dividing by zero).Therefore, the next step in the programis an error checking routine performedby the virtual instrument symbolizedby the zero check icon.

Figure '7 shows the diagram for thezero checker. To trap negative or in-

0742-32338840.00 + 2.00 1988 BY NATIONAL RESOURCE FOR COMPUTFRS IN LIFE SCIENCE EDUCATION


systolicduration

SV

Pressure Pulse Model V.E. Diagram

SV

systolicduration

FIGURE 6. Diagram of the virtual instrument for ventricular ejection. Shown are details ofthe ventricular ejection calculation symbolized in Figure 4 by the V.E. icon. This instrumentgenerates the positive half of a sine wave during systole (true case) and sets ventricularejection to zero during diastole (flase case).

valid numbers, the numbers are testedagainst zero. All numbers greater thanzero are passed through the TRUEform of the case structure, whereasnumbers less than or equal to zero arereset to one.

After passing through the zero check-er, the arterial volume is simply divid-

Pressure Pulse Model

numeric!input

ed by the arterial compliance to deter-mine the instantaneous arterial pressurewhich is then registered on the strip-chart and temporarily stored in theshift-register (lower right border ofFigur' 4). To determine the systolicand diastolic pressures and the ampli-tude of the pressure pulse, the instanta-

Zero Checker Diagram

numeric!input

filfflastlFALSE

111:34

0? =

numericoutput

1E6°31numericoutput

FIGURE 7. Diagram of the zero checker. This is an error-trapping routine that eliminatesinvalid numbers such as negative arterial volumes and division by zero errors.

neous arterial pressures for each cardi-ac cycle are accumulated in an array bythe virtual instrument symbolized bythe wave analyzer icon.

The diagram for the wave analyzer isshown in Figure 8. Recall that thetimer sets n equal to zero at the begin-ning of each cardiac cycle. The waveanalyzer consists of a TRUE-FALSEcase structure driven by the currentvalue of n. At the beginning of eachcardiac cycle (when n equals zero), thearray of instantaneous pressures accu-mulated by the array builder during theprevious cycle is processed by the"max-min" function (a menu-availablefunction in TabView) thus retrievingthe systolic znd diastolic pressures.The amplitude of the pressure pulse issimply the systolic minus the diastolicpressure. The current instantaneousarterial pressure is wired to an emptyarray builder to become the first ele-ment of the new cycle's array which isthen passed out of the TRUE form ofthe case structure to the shift-registeron the right border of the While-Wensloop. When n is greater than zeroduring each subsequent iteration of thecycle, the FALSE form of the casestructure uses a single array builder toadd each new instantaneous pressure tothe accumulating array until n is againset to zero by the timer.

EVALUATIONFor the life scientist, even one reason-ably adept at computer programming,constructing a mathematical model hasusually been tedious and time consum-ing because he had to develop specificroutines for data input and output, forapproximating nonlinear functions, andfor integrating time-dependent varia-bles. Simulations, particularly those onmainframe computers, were generallywritten in compiled languages that didnot have graphics and that often pre-vented interactive use of the model.Even with the advent of microcomput-ers, the investigator had to expendmore time and effort on supportingsoftware for graphics and interactiveutilities than on the model itself. There-fore, when we became aware of Lab-View, our hope was that a graphic pro-gramming language would make mod-eling more intuitive and much easier

1988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENCE EDUCATION X42-3233/88/30.00+ 2.00


for the life scientist than it had been inthe past.

In evaluating LabView, we used sev-eral criteria. First, we felt that the usershould only have to build his model,not develop graphics or interactive rou-tines. Second, the user should be ableto run the simulation continuously andobtain results in a readily comprehen-sible form such as strip-charts or X-Yplots. Third, the simulation should beexecuted in an acceptable amount oftime. Fourth, the model should be in-teractive to the extent that the modelercan easily interrupt the simulation tochange a variable's value and resumethe simulation. Similarly, altering themodel itself and beginning a now simu-lation shoitld be simple and straightforward. Finally, the simulation lan-guage should have all the arithmetic,transcendental, and matrix math rou-tines that high-level languages usuallyoffer. Lab View satisfies many of thesecriteria.

Programming EnvironmentThe metaphor of a virtual instrument,with controls and indicators on a frontpanel aad an executable block diagram,is a concept readily appreciated bymost physiologists. Indeed, the ease ofpresenting both input and output data ingraphic, easily assimilated forms is per-haps the best feature of Lab View. Infact, largely because of Lab View's vir-tual instrument approach to program-ming, it was surprisingly easy to ex-ploit Lab View's ability to display datagraphically and to implement the tworitodels we described. Starting from ashort list of values for constants and theinputs and outputs needed for the frontpanel, only three hours of programmingwere required to create an operationalversion of the simple cardiovascularmodel. To develop a similar model inBASIC with the same capabilities foruser interaction and the same highquality graphics would have requiredmuch more time and programmingexpertise.

Front PanelAlthough the two models we presentedmake limited use of the many availablefront panel controls and indicators, itshould be noted that all of the controls

Pressure Pulse Model Wave Analyzer Diagram

Old PressureArray

Pressure

n igLij1-110?

Old PressureRrray

TRUE

SystolicPressure

Vn[12=4

DiastolicPressure

PressurePulse

tma

=411

ID

New PressureArray

New PressureArray

FIGURE 8. Diagram of wave analyzer. At the beginning of each cardiac cycle (true case),this instrument uses the "min-max" function to select the diastolic and systolic pressures froman array of pressure values stored during the previous cardiac cycle (false case).

and indicators in LabView are menu-selected with specified ranges andscales that can easily be ;hanged by theuser (though not while a program isrunning). Range checking is doneautomatically, and out-of-range errorscan be ignored, coer...ed within thespecified range, or allowed to stop theprogram (as determined by the user).Positioning and changing the size ofthe controls and indicators to acheiveaparticular layout on the front panel arerelatively simple operations that requiresome familiarity with the Macintoshmouse. However, there is nu provisionfor customizing controls or indicatorsor for adding additional graphics to thefront panel (eg, it is not possible to adda schematic illustration of a model tothe front panel).

P.lock DiagramThe block diagram is analogous to theprogram code in other high-level lan-guages. In a small program such as thesimple cardiovascular model (Figure2), the diagram is truly self-explanatoryexcept for one or two LabView idio-syncrasies like the shift-registers (theonly way to use variable values recur-

sively). However, even slightly morecomplicated programs like the pulsepressure model (Figure 4) can be diffi-cult to decipher and debug. Their inde-cipherability sterns partly from the in-clusion of virtual instruments withinthe main block diagram. The blockdiagrams of such instruments withininstruments cannot be viewed at thesame time that the overall diagram isexamined. Futhermore, case structuresand other program control devices aresymbolized by several layers of figures,only one of which can be viewed on thescreen at a time.

Constructing a comprehensible Lab-View block diagram requires a sense ofspatial organization and some artistry.In LabView block diagrams, data flowoccurs most naturally from left to rightbecause the input terminals of functionicons are on their left, and their outputsare on the right. The orientation of theicons cannot be changed. A furtherconstraint is the small size of the MacPlus screen. Therefore, some fore-thought is necessary to anticipate theoverall layout of the diagram and theamount of space that the various func-tion icons will occupy on the screen.

0742-3233/8840.00 + 2.00 1988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFESCIENCE EDUCATION


Failing to plan ahead inevitably leadsto sphagetti-like tangles of wire thatcannot easily be debugged. Evenworse, editing such poorly planned dia-grams requires completely "un-wiring"the entire diagram just to make a fewsimple changes.

In Lab View block diagrams, fourdifferent types of data are depicted bywires with different appearances. For-tunately, Lab View does not make theprogrammer specify the type he needsbut instead makes the appropriate se-lection automatically depending, forexample, on whether the selected func-tion requires a simple variable or anarray. Logical errors and connectionsbetween inappropriate devices are im-mediately indicated by "broken wires,"an error message that often fails to pin-point the problem.

Execution SpeedWe have not quantified the executionspeed of LabView, but our impressionis that it runs at about the same speedas an interpreted BASIC. Thus, execu-tion speed is a potential problem. Onthe Mac Plus, the pulse pressure modelexecuted at an acceptable rate, but eachiteration produced a perceptible jic asthe strip-chart simulator scrolled &fossthe monitor. By contrast, on the fasterMacintosh IL the arterial waveformglided smoothly across the screen "likethe pulse of a perfect heart." There-fore, to maintain acceptable speed withlarger models than those presentedhere, it may be necessary to run thesimulations on a Macintosh II. Alter-natively, the program could be com-piled. The man facturer claims to havea complier for Lab View, although it isnot yet available commercially.

ConclusionsBefore using it, we had the impressionthat Lab View would make mathemati-cal modeling possible for all life scien-tists who are not computer program-mers. In most respects, LabView livedup to this expectation, but to some ex-tent it did not. Certainly, the many use-ful features of LabView, such as themath routines and the graphic displays,can easily be used by the unsophisti-cated programmer. Similarly, simplediagrams are indeed self-explanatory

programs. In our opinion, an inexperi-enced programmer could construct amuch more sophisticated model inLabView than in other high-level lan-guages. However, more elaborate Lab-View programs can be as difficult todecipher and debug as those in otherlanguages. Similarly, like other com-puter languages, LabView can demandarcane programming tricks, and themethods that have to be used to storevariable values and to branch on a par-ticular condition are often not straight-forward. Nevertheless, even the ac-complished programmer will find muchto admire and use in LabView: arith-metic and logical functions, array arith-metic, matrix and vector algebra, com-plex numbers, statistical functions, andsignal processing routines includingfast Fourier transforms. All of theseroutines appear as simple icons thatnaive and sophisticated programmersalike can easily incorporate into theirmodels. Therefore, considering theseand the previously mentioned advan-tages of LabView, we recommend thatLabView be seriously considered as anovel modeling methodology with thepotential of enabling the average lifescientist to simulate complex biologicalsystems.

To obtain a copy of the LabView programs pre-sented here, send the authors an 800k, 3.5 inchdisk and an appropriate, stamped, self-addressedmailing contair.er.

This work was supported by USPHS grants HL-36080 and AM-33024.

REFERENCES1. Coleman, TG, and FR Sias. Simulation of

biological systems. DECUS. DigitalEquipment Corp., Maynard, MA, 1969,pp.1-18.

2. Guyton, AC, AW Lindsey, and BNKaufman. Effect of mean circulatoryfilling pressure and other peripheralcirculatory factors on cardiac output.Amer. J. Physiol. 180:463-468, 1955.

3. Randall, JE. Microcomputers and physio-logical simulation, second ed., RavenPress, New York, NY, 1987, pp.207-219.

4. Rothe, CF. A computer model of the car-diovascular system for effectivelearning. The Physiology Teacher 22:29-33, 1979.

5. Vose, GM and G Williams. LabView:laboratory virtual instrument engineering

workbench. Byte 11(9):84-92, 1986.6. Wolfe, R. Block diagrams an :^ons

alleviate the customary pain of program-ming GPIB systems. Electronic Design34:125-132, 1986.

ISUBSCRIMON INFORMATION

Computers in Life Science Education ispublished monthly by National Resourcefor Computers in Life Science Education,Mail Stop RC-70, University ofWashington, Seattle, WA 98195.Subscription rate is $30.00 for 12 issues,including postage and handling in theUnited States and Canada. Add $20.00for postage (airmail) in Mexico andEurope and $23.00 for the rest of theworld.

This newsletter has been registered withthe Copyright Clearance Center, Inc.Consent is given for copying of articlesfor personal or internal use, or for thepersonal or internal use of specific clients.This consent is given on the condition thatthe copier pay through the Center the per-copy fee stated in the code on the firstpage for copying beyond that permittedby the US Copyright Law. If no codeappears on an article, the author has notgiven broad consent to copy andpermission to copy must be obtaineddirectly from the author. This consentdoes not extend to other kinds of copying,such as for general distribution, resale,advertising and promotional purposes, orfor creating new collective works.

Address orders, changes of address, andclaims for missing issues to NRCLSE,Mail Stop RC-70, University ofWashington, Seattle, WA 98195. Claimsfor missing issues can be honored only upto three months for domestic addressesand six months for foreign addresses.Duplicate copies will not be sent toreplace ones undelivered due ra failure tonotify NRCLSE of change of address.

Address editorial correspondence toHarold I. Modell, PhD, NRCLSE, MailStop RC-70, University of Washington,Seattle, WA 98195. (BITNETMODELL@ UWALOCKE)

POSTMASTER: Send address changes toComputers in Life Science Education,NRCLSE, Mail Stop RC-70, University of,Washington, Seattle, WA 98195.

01988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENCE EDUCATION1110/* 0742.3233/88/S0.00+ 2.00

Li7

VOLUME S NUMBER 8, AUGUST 1988

I

CLSEE3 5(8)57 - 64,1986 ISSN 0742-3233

Am': 111111110111111111111

HAROLD I. MODELLDepartment of RadiologyUmversity of WashingtonSeattle, Washington

MARCEL BLANCHAERDepartment of BiochemistryFaculty of MedicineUniversity of ManitobaWinnipeg. Manitoba

THEODORE J. CROVELLOGraduate Studies and ResearchCalifornia State University, Los AngelesLos Angeles. California

JAMES W. ECKBLADDepanment of BiologyLuther CollegeDecorah, Iowa



'TERRY M. MIIUTENGraduate School of Ihernedical SciencesUniversity of Texas Health Science CenterSan Antonio, Texas

JAMES E. RANDALLrtment of Physiology

I a UniversityBloomington, Indiana




National Library of MedicineBethesda, Marylan 1



NRCLSE

CONTENTS

USE OF A COMPUTER SIMULATION TO REINFORCEWET LABS IN NUCLEAR MEDICINE

Harold I. Modell and Michael M. Graham

THE BULLETIN BOARD

CAN YOU HELP?

57

61

63

USE OF A COMPUTERSIMULATION TO REINFORCEWET LABS IN NUCLEARMEDICINE

Harold I. Modell and Michael M. GrahamDepartment of Radiology, University of Washington, Seattle, Washington

Many procedures commonly encoun-tered in Nuclear Medicine are indicatordilution determinations. A knownquantity of a radionuclide, for example,is introduced into an unknown volume.By comparing the activity within analiquot of the unknown to that of astandard, the unknown volume can becalculated. The samples are gt,nerallycounted in a well counter. If these de-terminations are to be carried out accu-rately and interpreted properly, it isessential that Nuclear Medicine tech-nologists and physicians understand the

conservation of mass principles under-lying indicator dilution techniques aswell as the errors that can arise as aresult of well counter characteristics.The American Board of Nuclear Medi-cine recommends that all residenttraining programs include principlesand limitations of both indicator dilu-tion techniques and well counters.

In our resident program, these issueshave been addressed through a series oflaboratory exercises conducted onceeach year. To ensure that the lab ses-sions do not interfere with Nuclear

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58 COMPUTERS IN LIFE SCIENCE EDUCATION, VOLUME 5, NUMBER 8, AUGUST 1988

Medicine clinic activities, a finite timecommitment must be made to deal withvarious logistical problems related toequipment, space, and scheduling. Al-though some residents wish to explorethese topics further during the remain-der of the year, they have been unableto do so because of time and space con-straints. To overcome this limitation,we have developed a simulation of awell counte:, designed for use currentlywith Apple II computers, that allowsthe user to explore indicator dilutionprinciples and the effects of countercharacteristics on counting efficiency.

WELL COUNTER DESIGNThe "standard" well counter currentlyused in Nuclear Medicine environ-ments ce-isists of a sodium idodide(Nal) crystal with a hole in it, a photo-multiplier tube, a high voltage source,an amplifier, and a multi-channel anal-yzer. The gamma rays from a samplein the hole interact with the crystal,causing flashs of light. The brightnessof each flash of light is proportional tothe energy of the gamma ray. The lightis sensed by the photomultiplier tuberesulting in a small eleuirical pulse thatis propotional to the brightness of theflash of light. The small pulse is am-plified and sent to the multi-channelanalyzer, which, along with its elec-tronics, displays the data.

Counts obtained with the Nal crystalwell counts r are subject to a variety offactors. Some of these are related to

THE FOLLOWING ISOTOPE STANDARDS ARECURRENTLY AVAILABLE FOR YOUR USE:

1 ) COBALT-572) COBALT-603) CROMI UM-514) GALLIUM-675) INDIUM-1116) IODINE-1257) IODINE-1318) TECHNETIUM -99M9) YTTERBRI UM-169

CHO I CE?

FIGURE 1. Initial screen of well counter simulation listing the isotopes that can be counted.

the Nal crystal and to the electronicsprocessing the detected events, somedepend on the sample size and thegeometry of the counting system, andothers are dependent upon the randomnature of radioactive disintegrations.The counting efficiency reflects theinteraction of these factors. To gainmeaningful information when usingsuch an instrument, it is necessary toundeo.t.and the factors contributing tothe counting efficiency.

The wet labs in our curriculum are de-signed to help the residents examine anumber of these factors including posi-tion of the sample within the detectorchamber, sample volume, influence ofthe container in which the sample isplaced, and setting the countingwindow.

.. "A"e, ;

SCALE MAXIMUM (REV)? 800

WELL COUNTER SIMULATIONThe well counter simulation provides ameans by which students can extendtheir wet lab experience to furtherexplore indicator diluticn principlesand the effects of counter characteris-tics on counting efficiency. Dead timelosses and Poisson statistics are incor-porated into the counting scheme.Counting characteristics of the simu-lated counter were drawn from datadescribed by Hine' and Sorenson andPhelps?

Setting up the counterWhen using the simulation, the studentis presented first with a list of stan-dards, shown in Figure 1, that areavailable for setting the counting win-dow. The digitized energy spectrum

.....

SCALE MAXIMIH (REV)? 500

FIGURE 2. Energy spectra for Iodine-131. In the left panel, the student has chosen 800 Key as the scale limit. In the right panel, thespectrum is shown with 500 Key as the scale limit. The process can be repeated as many times as desired.

0 1923 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENCE EDUCATION 0742-3233/8840.00+ 2.00

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COMPUTERS IN LIFE SCIENCE EDUCATION, VOLUME 5, NUMBER 8, AUGUST 1988 59

for each of the standards is stored in aseparate file and accessed by theprogram as needed. After choosing thedesired standard, the student is pre-sented with an energy scale on whichthe energy Fpectrum of the chosenstandard will be displayed. The studentcan adjust the energy scale (Fig= 2)to best display the spectrum for settingthe desired counting window.

The counting window is then set byusing the arrow keys to move a hairlineto the desired positions on the spectrum(Figure 3). If thc.- chosen window isunsatisfactory, it can be reset.

Sample preparationHaving defined the counter set-up, thestudent must prepare a sample to count(Figure 4). In doing so, the amount ofisotope to be used as the indicator andthe volume in which it is to be diluted

'are defined. At this point, a message ispresented reminding the student thatthe basic underlying assumption in allindicator dilution determinations is thatthe volume in which the indicatorresides is well mixed.

The next step is to define the samplevolume that is to be put into the counterand define the counting time.

Counter dataWhen sample preparation is complete,and counting parameters have been de-fined, the program reviews the sampledefinition and counter parameters and

. - ...... ,,,,,,

SET WINDOW UPPER LIMITUSE < > KEYS TO ADJUST HAIRLINEPRESS 'A' TO ACCEPT POSITION

FIGURE 3. Spectrum of Iodine-131 with hairlines indicating the energy limits chosen forthe counting window. The lower limit has been defined.

presents the results of the countingprocess (Figure 5). This screen alsocontains information about the count-ing efficiency. It presents the apparentcourting efficiency (total counts detec-ted relative to the total theoreticalcounts that should have been detected)as well as the actual overall efficiency(apparent counting efficiency correctedfor background).

OptionsAfter the counting data have been pre-sented, the student is presented with anoptions menu (Figure 6) that allowsredefinition of the tracer, counter pa-rameters, and sample parameters. At

SAMPLE PREPARATION

AMOUNT OF 1131 TO USE AS INDICATOR (MCI)?5

VOLUME FOR DILUTION (L)?1

ASSUMPTIONVOLUME WITH INDICATOR IS WELL MIXED!

SAMPLE VOLUME FOR COUNTER (ML)?1

COUNTING TIME (SEC) ?30

FIGURE 4. Interactive screen on which the student has defined how the sample is to beprepared.

this point, the choice to count the samesample again is also provided. In thisway, the random variation in countsseen with actual well counters can beexamined. For example, Table 1 liststhe counts recorded when the samesample of kdine-131 was counted fivetimes.

Error messagesError messages presented to the studentam designed to promote an additionallearning experience by providing addi-tional information relevant to theerror.In some cases, the error relates to aspecific choice made by the student.For example, if the student choses tocount the sample for less than 10 sec-onds, a message is presented indicatingthat a longer count time will providebetter statistical data. In other cases,the error results from the counter char-acteristics or from a combination ofchoices. For example, if the count ratefrom the sample is too high for thecounter, a message appears indicatingthat the counter is paralyzed.

MODEL SOLUTIONData used for incorporating the effects

of the various counting factors such asthe effects of counter deadtime andsample size were obtained from chap-ters on well counters presented byHine' and Sorenson and Phelpsl. Sev-

0742:3233/88/S0.00+ ZOO CD 1988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENCE EDUCATION

60


ISOTOPE: 1131

COUNTER SETTINGS:

Table 1. Counts obtained from repeatedcounting trials of sample described inFigure 4.

WINDOW = 342 TO 384 KEVCOUNTING TIME =30 SEC

Trial

1

Counts detected

403,070SAMPLE PREPARATION: 2 402,2285 MCI IN 1 L 3 402,567

4 402,905SAMPLE = 1 ML 5 403,205

TOTAL COUNTS DETECTED = 402858

APPARENT COUNTING EFFICIENCY = 19 %

ACTUAL OVERALL EFFICIENCY = 7

(PRESS ANY KEY TO CONTINUE)

FIGURES. Output showing the results of the well counter count.

eral factors enter in to calculating thecounts detected for a given sample.Initially, the actual activity (mCi) perml of sample is calculated. Using thisvalue, the theoretical counts /se.: foreach ml of sample are calculated ac-cording to the following equation.

OPTIONS:

1) CHANGE ISOTOPES

2) RESET WINDOW

3) CHANGE AMOUNT OF INDICATOR

4) CHANGE VOLUME FOR DILUTION

5) CHANGE SAMPLE VOLUME

6) DILUTE SAMPLE VOLUME

7) CHANGE COUNTING TIME

8) COUNT SAMPLE

9) QUIT

CHOICE?

FIGURE 6. Options menu from wellcounter program.

Counts/sec = mCi x (3.7 x 107disintegrations/sec) xCounting efficiencyof the isotope

To obtain the counting efficiency ofeach isotope, the gamma ray efficiency/disintegration is multiplied by thequantum detection efficiency for eachgamma ray produced by the isotope.The resultant values are then summedto obtain the counting efficiency.'

The value obtained for counts/sec isthen adjusted for the sample volume ifthe sample volume is greater than 1 mland for samples that have been diluted'

The influence of deadtime is then in-corporated into the calculation accord-ing to Sorenson and Phelps.2

Background activity, calculated as .35counts/sec per Key in the counting win-dow, is the next factor added to thecounts detected.

The total counts reported are calcu-lated from the following equation.

Total counts = ((counts/sec x % spec-trum that represents

the counting window)backgound) x total

counting time.

This value is then adjusted by adding arandom gaussian variation to it.

The total theoretical counts thatshould have been detected (used to pre-sent the actual overall efficiency to thestudent) is calculated from the actualactivity per ml of sample, the definitionof a mCi of activity, the counting time,the fraction of the energy spectrumwithin the counting window, and thesample size being counted.

EVALUATIONA systematic evaluation of the effec-tiveness of this program has been dif-ficult to perform because the number ofnew residents exposed to the materialeach year is very small, normally lessthan five, and the program is availableon an optional basis. Nevertheless,residents who have used the programhave indicated that they have found ithelpful in reinforcing the materialpresented in the wet labs.

REFERENCES1. Hine, GI. 7-Ray sample counting. In:

Hine, GJ ed. Instrumentation in NuclearMedicine. New York, Academic Press,1967, p. 275-291.

2. Soren-on, JA and ME Phelps. Physics inNuclear Medicine. New York, Grune &Stratton, 1980.

01988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENCE EDUCATION 07424233/8840.00 + 2.00

COMPUTERS IN LIFE SCIENCE EDUCATION, VOLUME.s, NUMBER 8, AUGUST 1988

THE BULLETIN BOARDThe Bulletin Board is published period-ically to inform readers of upcomingmeetings of interest. If you know ofmeetings, symposia, continuing educa-tion courses, etc, of interest to lifescience educators, and they do notappear in The Bulletin Board, please letus know. Send pertinent informationto: Dr. Harold Modell, NRCLSE,RC-70, University of Washington,Seattle, WA 98195, or let us know viaBITnet. NRCLSE's BiTnet address is:MODELL@UWALOCKE

AUGUST 24-26, 1988. 10th AnnualConference. Interactive Videodisc inEducation and Training. Washington,D.C.

Contact:

Society for Applied LearningTechnology

50 Culpeper StreetWarrenton, VA 22816(800) 457-6812In VA: (703) 347-0055

OCTOBER 6- 8,1988. Health Care1988: The Challenge of ChangeMorgantown, West Virginia.

Contact:

Janet WangSchool of NursingWest Virginia UniversityMorgantown, WV 26506(304) 2934297.

OCTOBER 14-15, 1988. Fall, 1988Semi-Annual Meeting. Association ofBiologists for Computing. San Jose,California.

Contact:

Charles W. Bell, PresidentAssociation of Biologists for

ComputingDepartme of Biological SciencesSan Jose State UniversitySan Jose, CA 95192

NOVEMBER 6- 9,1988. The 12th AnnualSymposium on Computer Applicationsin Medical Care (SCAMC). Washing-ton, D.C.

Contact:

Robert A. Greenes, MDProgram ChairSCAMC Of of CMEThe George Washington UniversityMedical Center

2300 K Street, NWWashington, D.C. 20037(202) 994-8928

NOVEMBER 7- 10,1988. Association forthe Development of Computer- BasedInstructional Systems (ADCIS)30th International Conference. Phila-delphia, Pennsylvania.

Contact:

ADCIS, 409 Miller HallWestern Washington UniversityBellingham, WA 98225(206) 676-2860

NOVEMBER 10-11, 1988. First AnnualMeeting. Medical Interactive VideoConsortium. Washington, D.C.

Contact:

J. Henderson, MD, MIVCUniformed Services University of the

Health Sciences4301 Jones Bridge RoadBethesda, MD 208144799

61

JANUARY 30 - FEBRUARY 2,1989.1989 Florida Instructional ComputingConference. Orlando, Florida.

Contact:

Florida Department of EducationOffice of Educational TechnologyKnott BuildingTallahassee, FL 32399(904) 488-0980

MAY 15- 19,1989. Medical Informaticsand Education: An InternationalSymposium. Victoria, B.C., Canada.

Contact:

Conference ServicesDivision of University Extension and

Community RelationsUniversity of VictoriaP.O. Box 1700Victoria, B.C. V8W 2Y2Canada(604) 721 -8475

OCTOBER 1 -20, 1989. MEDINFO 89.The Sixth World Congress on MedicalInformatics. Beijing, China.

Contact:

Phil R. Manning, MDChairman, Scientific Program

CommitteeMEDINFO 89USC School of Medicine1975 Zonal Avenue, KAM 317Los Angeles, CA 90033(213) 342-9370

Gao Pengyuan, MD or Ms Shan HuiginChina Medical Informatics AssociationMEDINFO 89 Secretariat55 Xueyuan Nanlu, Wei Gong CunHaidian District, PO Box 8139Beijing, China 100081

0742-:233/88/50.00 + 2.00 .0 1988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENCE EDUCATION

62


APPLE II? IBM -PC? MACINTOSH? ... NO PROBLEM!

SIMULATIONS IN PHYSIOLOGYThe Respiratory System

Harold 1. Modell, Ph.D.School of Medicine, University of Washington, Seattle,

Series of 12 Simulations in RespiratoryPhysiology

117 Page Laboratory Manual

Designed for use in student laboratory andgroup discussion environments

Pictorial outputs designed to provideconceptual aid, show where variable valuesare measured, or illustrate the model andhow it is solved

Tabular outputs allow comparison of datafrom up to 7 'experiments'

Available for Apple II, IBM-PC (MS-DOS)compatible, and Macintosh (requiresMicrosoft BASIC) computers

Purchase includes permission to make enoughcopies to supply appropriate studentpopulations

ORDERING INFORMATION

Computer_Procrrams*12 Simulations written in BASICDocumentationOne copy of student laboratory man:41

. . . $100*IBM-PC version uses CGA graphicsMacintosh version requires Microsoft BASIC

SlackilliahOMIozugalivalSQuantities of 1-25 . . . . $10 eachAdditional copies . . . . $8.50 each

Address orders toNRCLSEMail Stop RC-70University of WashingtonSeaule, WA 98195

Be sure to indicate which version you need!

PROGRAM OVERVIEW

Static Relationships

Dynamic Relationships I

Work of Breathing

Dynamic Relationships II

Material covered

MECHANICS

Elastic properties of the lung, chest wall, andtotal respiratory system

Effects of lung compliance and airwayrcsistqnce on tidal volume development

Oxygen cost of elastic, resistive, and totalwork during inspiration

Respiratory dynamics of the total respiratorysystem

GENERAL GAS EXCHANGE

Alveolar Gas Exchange

0, and CO, DissociationCurves

Exchange from Atmosphere toTissues

Gra exchange between the atmosphere andalveolar re-,ervoir

Interrelationships between the 02 and CO,dissociation curves

Influence of alveolar ventilation, cardiacoutput, and anatomic shunt flow on arterialblood composition and gas exchange at thetissues

Chemoregulation of Respiration Influence of chemoreceptor sensitivity andrespiratory mechanics on 02 and CO,response curves

VA/Q RELATIONSILTPS

Gas Exchange in a Single Effects of ventilatic- -perfusion ratio andAlveolus inspired gas composition on exchange in a

single exchange unit

The Non - Uniform Lung

Overall Gas Exchange

Gas exchange from atmosphere to tissueswith VA/Q mismatching in the lung

Gas exchange from atmosphere to tissueswith VA/Q mismatching and a true shunt

ACID-BASE BALANCE

Acid-Base Balance: Fundamen- Acid-Base balance from a Base Excesstal Relationships viewpoint

©1988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENCE EDUCATION

R30742-3233188/SO.00 + 2.00

COMPUTERS IN LIFE SCIENCE EDUCATION, VOLUME 5, NUMBER 8, AUGUST 1988 63

CAN YOU HELP?Among the major goals of NRCLSE are to help life science educators identify colleagues who share a common interest in theuse of the computer as an educational tool (see Annual Colleague Directory, February - April, 1988 CLSE) and to help lifescience educators identify appropriate software for use in their curricula.

In an attempt to achieve the latter, we routinely publish software lists (see Where's the Software, May - June, 1988 CLSE).However, these lists do not offer any information regarding the applicability of programs to specific curricular needs. Toprovide this type of information, we need your help. Please take a few moments to let us know what software works for you.Providing information in the format presented below will help us to organize this information to share with colleagues.Mail information to NRCLSE, Mail Stop RC-70, University of Washington, Seattle, WA 98195. Please include yourname, address, phone nubmer, and BITnet address (ifapplicable) with your response.

SOFTWARE INFORMATIONREPORTName of software:

Source of software:

Type cf program:

Tutorial (Q & A) Simulation Combination

Equipment th, !ded to run progam:

Apple II IBM-PC Macintosh Mainframe

Student population using software:

Undergraduate Graduate Medical/Veterinary Dental

Content area covered by this software:

How do you use this software:

Independent study

Other

Nursing

Classroom instruction Student laboratoryWould you be willing to discuss your use of this program with colleagues?

Yes No

What do you like best about this software?

What do you like least about this software?

0742-3233/88/SO.00 + 2.00 0 1988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENC .DUCATION


IN,

AIMS AND SCOPE The goal of Computers in Life Science Education is to provide a means of communicationamong life science educators who anticipate or are currently employing the computer as aneducational tool. The range of content includes, but is not limited to, articles focusing oncomputer applications and their underlying philosophy, reports on faculty/studentexperiences with computers in teaching environments, and software/hardware reviews inboth basic science and clinical education settings.

.11MINIMMINII :......INVITATION TO CONTRIBUTORS Articles consistent with the goals of Computers in Life Science Education are invited for

possible publication in the newsletter.


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T:tie page should in;lude full title, list of authors, academic or professional affiliations, andcomplete address and phone number of the corresponding author.


Reference style and form should follow the "number system with referencesalphabetized"described in the Council of Biology Editors Style Manual. References should be listed inalphabetical order by the first author's last name, numbered consecutively, and cited in thetext by these numbers.



SUBSCRIPTION INFORMATION Computers in Life Science Education is published monthly by National Resource forComputers in Life Science Education, Mail Stop RC-70, University of Washington, Seattle,WA 98195. Subscription rate is 530.00 for 12 issues, including postage and handling in theUnited States and Canada. Add $20.00 for postage (airmail) in Mexico and Europe and$23.00 for the rest of the world.


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0 1988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENCE EDUCATION 0742-3233788/$0.00 + 2.00

65

VOLUME 5 NUMBER 9, SEPTEMBER 1988

COMPUTERSIN LIFF,16SCIENCEEDUCATION

HAROLD I. MODELLDepartment of RadiologyUniversity of WashingtonSeattle, Washington


THEODORE J. CROVELLOGraduate Studies and ResearchCalifornia State University, Los AngelesLos Angeles, Calirornia

JAMES W.-ECKBLADDepartment of BiologyLuther CollegeDecorah, Iowa



TERRY M. MIKITENGraduate School of Biomedical SciencesUniversity of Texas Health Se.mce CenterSan Antonio, Texas

JAMES E. RANDALLDepartment of Physiology1,-nana UniversityBloomington, Indiana

PATRICIA SCHWIRIANCollege of NursingOhio State UniversityColumbus, O'tio




DOROTHY WOOLEY -McKAYDepartment of BiologyGlendale Community CollegeGlendale, Arizona

GLEN YOSHIDADepartment of Life SciencesLos Angeles Southwest CollegeLos Angeles, Califamia

NRCLSE

CISEE3 5(9), 65- 72,1988 ISSN 0742-3233

111111111111111111111111111111

CONTENTS

USING STELLA SIMULATION SOFTWARE INLIFE SCIENCE EDUCATION

Thomas G. Coon

1011111IMINICI

65

USING STELLA SIMULATIONSOFTWARE IN LIFE SCIENCEEDUCATIONThomas G. CoonSchool of Forestry, Fisheries and Wildlife, University of Missouri, Columbia, Missouri

Mathematical modeling has a richpotential for use in teaching the lifesciences. While traditional approachesmay emphasize memorization of struc-tures and functions, simulation allows astudent to develop his or her under-standing and analysis of living systemsby testing hypotheses about how the.systems work. In the past, simulationhas been unapproachable in other thanadvanced undergraduate or graduatelevel courses, mainly for logistical rea-sons. The use of modeling in teachingis dependent on students 1) having ac-cess to computer facilities adequate to

handle many iterative calculations andlarge data sets and 2) being facile pro-grammers. Before the development ofpersonal computers with large memoryand high speed, students were limitedto the use of mainframe computers andstandard multi- purpose programminglanguages such as FORTRAN. Whilethe newer personal computers have suf-ficient speed and memory to serve eventhe sloppiest of modelers, they did noteliminate the need for students to beprogrammers before becoming mod-elers. It was nearly inevitable that asimulation class would devolve into a

EDITOR'S NOTEDue to space limitations, Keeping Abreast of the Literature,CLSE's regular quarterly feature due to appear in this issue willappear in next month's issue.

0742-323318840M + 2.00 0 1988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENCE EDUCATION

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66 COMPUTERS IN LIFE SCIENCE EDUCATION, VOLUME 5, NUMBER 9, SEPTEMBER 1988

programming class; the students woulddevelop their programming skills, butrarely to the level at which they coulduse those skills to build, test and usetheir own models.

Several solutions have develooed toalleviate the need for students in bio-logical modeling to be expert program-mers. One solution is custom-madesoftware packages that are programmedto run simulations on particular sub-jects, such as those described by Eck-blad' Another, more general solutionis a programming language designedfor modeling that students can learnand use on personal computers. Onepackage designed to meet this need isSTELLA, developed by Barry Rich-mond and others at High PerformanceSystems, Inc? STELLA was designedby Richmond et al' to make modelingavailable as a learning tool for studentsof all disciplines. The name STELLAis an acronym for Structural Thinking,Experiential Learning Laboratory withAnimation, a cumbersome explicationthat is less prominently expressed ir,later versions of the user's guide. Thecreators of this software state that theirmotivation for developing STELLAwas to make the discovery approach toany discipline of study more accessibleand more fruitful.. Although this arti-cle does not address all disciplines,STELLA is a resounding success forteaching the dynamic aspects of the lifesciences. It is important to keep inmind that the reason for using modelingas a teaching tool is not to make it eas-ier for students to make predictionsabout the outcome of life processes, butto make it possible for students to de-velop their understanding of life proc-esses. Students can effectively accom-plish this goal if they are challenged to1) state their understanding of the dy-namics of a living system in a struc-tured format (ie, a model), and 2) testtheir understanding by asking questionsof their model using a structured, ex-perimental procedure. STELLA allowsstudents to carry out both of thesesteps.

STELLA is designed to operate onMacintosh computers (512K memoryor greater), and it takes full advantageof the distinctive Macintosh user inter-

face. It allows a user to begin with aconceptual model of a system andmove through the standard steps ofmodel building and testing until themodel is ready to use as an experimen-tal instrument of study. It reinforcesthe standard modeling process of build-ing a conceptual model, making themodel quantitative, validating the mod-el, and then using the model as a studytool .2 Furthermore, it keeps the userhonest in that the model will not workunless every parameter is defined andconnected to the rest of the model. It isfriendly enough to inform the errantuser of the parameters that remain to bedefined.

As is true for any good software,STELLA has been modified and up-dated several times since it becamecommercially available in late 1985.The initial version used some home-grown substitutes for standard Macin-tosh menu selections (eg, icons repre-senting sticks of dynamite substitutedfor the Cut editing command) and of-fered no nvenient means of transfer-ring mouel output to another file elec-tronically. Similarly, data could beentered into the model only by typingin changes to the initial conditions.The and other shortcomings havebeen eliminated in Version 2.0, re-leased in early 1988. The STELLAdevelopers have divided the initialversion into two packages designed fordifferent user groups, STELLA forBusiness and STELLA for Education.While the basic architecture is the samefor both packages, they differ in theselection of built-in functions and othersubtleties. For this review, I haveconfined my attention to the latter.

HOW IT WORKSAs in my modeling exercise, theSTELLA modeler begins by construc-ting a conceptual model that identifiesthe essential components of the modeland the important relationships be-tween them. I emphasize essential andimportant because a modeler must beable to sift through the multitude ofcomponents and factors inherent to acomplex system and focus on those thatare crucial to understanding the particu-lar dynamics of the system that interest

the modeler. The advantage of mod-eling is that it allows the user to simpli-fy the system sufficiently to understandand study it, but it challenges the mod-eler not to simplify 1-: the extent thatthe model no longer behaves in thesame way as the system. For example,in studying the redfish fishery in theGulf of Mexico, it may be interestingand true that the culinary and marketingskills of a New Orleans chef had a ma-jor impact on the fishery. Yet, for abasic understanding of how redfishpopulations sustain themselves in anenvironment that includes fishing nets,it is more important to include proces-ses such as redfish reproductive rates,mortality rates, and fishing rates thanYuppie economics. By simplifying themodel, the user can develop an under-standing of the system sufficient tolater ask the question, "What happens ifthe demand for redfish quadruples inone year, perhaps due to the success ofsome Cajun marketing?"

STELLA uses a hydraulic analogy forrepresenting the conceptual model onthe monitor screen. The basic elementsof the model are stocks, flows, convert-ers and input links. The icons for theseare illustrated in Figure 1. Stocks aredefined as components that can accum-ulate or lose their basic elements. In a

ElSTOCK

0 0stock2

FLOW_REGULATOR

0CONVERTER

convener2

converter) INPUT LINK

FIGURE 1. Icons used to build conceptualmodel diagrams in STELLA.

0 1988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENCE EDUCATION 0742-3233/880.00 + 2.00

COMPUTERS IN LIFE SCIENCE EDUCATION, VOLUME 5, NUMBER 9, SEPTEMBER 1988 67

hydraulic analogy, any vessel that cancontain water (eg, cup, water tower orocean) is a stock. In the rechish anal-ogy, a population of redfish could be astock, or the fleet of fishing boats maybe a stock. The stock analogy is a nat-ural for fishery population models,however a little imagination can extendit to any biological system. For thephysiologist, the accumulation oface-tylcholine in a pre-synaptic neuron orof free oxygen in erythrocytes may bestocks of interest. In constructing amodel with STELLA, the modeler be-gins by placing all of the pertinent

,stocks on the screen and naming them.For a stock to c.ceumulate or lose the

articles it holds, it must have a meansof passage for the articles into and outof the stock. STELLA use, "flows",hydraulic pipes with control valves("regulators"), to represent this func-tion. The stock representing a redfishpopulation might have flows cominginto it that represent births and immi-gration and flows going out of it thatrepresent natural mortality and fishingmortality (Figure 2). While the flowicons represent the .1cept of fishmoving into and out the population,they do not show en.4er where the fishcome from or go to, or what factorsdeie.rmine how many fish enter or leavethe population. The sources a1.,1 sinksof fish may be extraneous to the goal ofthe modeler, in which case, STELLAallows the modeler to be vague("clouds.' are at the appropriate end ofsuch flows). If the modeler does wantto designate the source of inflows oroutflows, she can do so by planting theappropriate source stock at the appro-priate end of the flow pipe. Ofcoursethis new stock may need some inflow/outflow designations, as well.

All varying flows, such as births orfishing deaths, are regulated by someadditional factors. They may be Cy-namic factors of critical importance tothe model (eg, water temperaturesduring the time of larval development)or other factors of unknown importanceto the model. STELLA incorporatesthe influence of these factors, usingicons for "converters", the factors, and"input links" that specify the directionof ;:et:on. In the redfish model, the

REUFISH_STOCK0BIRTHS DEATHS

0

FIGURE 2. Conceptual model of a simple population model showing the model stock,flows, and converters using STELLA.

temperature converter acts on the regu-lator of the birth flow (Figure 3). Otherfactors that influence birth rates may beadded to the model (eg, plankton abun-dance, larval abundance) as converters,ar taken from other parts of the modelalready :presented (eg, the populationstock). Furthermore, converters can beused to influence other convertersrather than flow regulators. All otherstocks, flows, conv.rters, and inputlinks can and shot' be added to thediagram on the monitor screen beforethe modeler goes on to define the quan-titative relationships between the con-verters and their objects of action.

STELLA allows the modeler to definethe quantitative aspects of the model ineither of two ways, by typing in anequation or by drawing a graphical rep-resentation of the function. For exam-ple, the modeler may not have a reli-able data set on the relationship be-tween the number of adult raffish andthe number of larval redfish they pro-duce each year, but she may have ageneral model for such a relationshipbased on studies of other species.Rather than worry about the details ofunknown regression coefficients, themodeler can simply draw a graphical

representation of such a relationshipusing the command "Become Graph"(Figure 4). On the other hand, themodeler may have derived a regressionequation for the relationship betweentemperature and plankton abundanceand the survival of redfish larvae to theend of their first year, based on years ofdata collection. The modeler can sim-ply type in the equation that expressestMs relationship to complete the inflowsizle of the model. A similar approachcan be used to complete the outflowside of the model. In defining equa-sons, a number of mathematical, logical, and trigonometric functions areavailable as "Built-ins" to he software.

Once the quantitative aspects of themodel have been completely defined,the modeler can view the structure ofthe model in one of two ways. TheDiagram window remains as the con-ceptual representation of the model,and the quantitative expression for eachrelationship can be viewed by openingthe converter or regulator of interest.For a complete list of the equations andgraphical relationships, the user canopen the Equation window. Bothwindows can be printed by a simplecommand.

REDFIS STOCKBIRTHS DEATHS

TEMPERAT E

PLANKTON

LARVAEHARVEST NA fURALMOR) iTY

FIGURE 3. Complete conceptual diagram of a population modal showing the convertersand input links added to the diagram.

0742-3233/8 840.00 + 2.0C e 1988 BY NATIONAL RESObit'E FOR COMPUTERS Bt LIFE SCIENCE EDICATION

R8


7.75LA

V 5.5A

E

6(x 10 ) 3.25

..

0 5 10 15

REDFISH_STOCK ( x 105 )

20

FIGURF 4. Graphical definition of the relationship between the converter "LARVAE" andthe stock "REDFISH_STOCK" using the operation "BECOME GRAPH" in STELLA.

At this point, the model is ready to berun. Before running the model, the userhas the opportunity to designate severalaspects of the simulation technique.All models are run as time-dependentsystems. Thus, the model begins withthe initial conditions and makes itera-tive calculations to determine how theflows and stocks will change over time.The modeler can designate one of threeintegration methods (Euler's, 2nd-orderRunge-Kutta, and 4th-order Runge-Kutta) as well as the increments of timeto use in calculations and the startingand ending time for the simulation run.

The value of any of the model stocksor converters can be produced as amodel output, and these can be pre-sented in user-defined graphic and tab-ular forms. The graphic output can bepresented as a time-series plot (Figure5a) or as a plot of one parameter rela-tive to another (Figure 5b). The userdefines which parameters to include inthe output table and at what time inter-vals they should be included (Table 1).Output graphs and tables can be viewedon the monitor and can be printed to anexternal device.

Version 2.0 allows the user to save a

Diagram window or an output graph asa bit-map file accessible by "paint" -type software or as a file of PICT ele-ments accessible by "draw"-type soft-ware. Tables anti *.;.? Equation windowcan be saved as text fires, accessible bymany applications, inclding word proc-essors and spreadsheets (tables only).After a model is oprative, the user

may need to make some adjustments toassure that the model behaves like thesystem it represents. Adjustments toequations can be made from theEquation window or by opening thestock, converter, or regulator of inter-est. Additional converters and inputlinks can be added and defined quickly,without redefining the rest of the mod-el. The different output options and theability to stop a simulation run before itis completed allow the uscr to evaluatethe effects of these adjuslinents on themodel's performance. Further tests,such as sensitivity analyses, can be per-formed by use of some built-in func-tions. Adding the PULSE function toan equation will cause the model tomake a one-time and large scale changein the value of a parameter used in thesimulation. Thus a pulse of plankton

could be included halfwa. through asimulation run to determine how themodel responds to a Jne-time iterationin the regulating conditions. Otherfunctions allow for continuous changein the value of a parameter over time(RAMP) or discrete step-wise changes(STEP).

One of the most valuable features ofSTELLA is the availability of twofunctions that introduce user-prescribedstochastic behavior in what wouldotherwise be deterministic models. TheRANDOM function will generate a ran-domly selected value between 0 and 1to use in describing random behavior ofa variable or function. The RANDOM-generated value can be modified byarithmetic transformation to obtain val-ues drawn from any continuous proba-bility function desired. For normally-distributed variation, the NORMALfunction can be used. The default nor-mal distribution has a mean of 0 anastandard deviation of 1, however theseparameters can be modified by adjust-ing the arguments of the NORMALfunction. These functions not onlyallow the user to observe the sensitivityof the model to variation in the modelcomponents, but they can be used tostudy the influence of natural variationon the behavior of the system beingmodeled. This is invaluable in helpingstudents to learn what levels of varia-tion are important in modifying systembehavior, as well as the particular pa-rameters that are most sensative tovariation.

EXAMPLES OF STUDENT PROJECTSIn the process of developing an ad-vanced graduate course in fishery man-agement, I was faced with the dilemmaof providing students an opportunity toplan and evaluate fishery managementplans without having the luxury cf rep-licable lakes, ponds, and streams oryears to carry out the experiments.Simulation seemed a viable means forthe students to develop their plans andto evaluate their consequences withinthe reality of a classr )am. A variety ofmainframe and microcomputer labfacilities are available at no additionalexpense to the students on our campus.However, I anticipated that few of the

©1988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENCE EDUCATION 0742-3233/88/S0.00 + ZOO

1


students would have the computer skillsnecessary to convert their models intoprogram code and feared that lab ses-sions would be little more than debug-ging sessions. With some frustration, Imade my course plans based on a tradi-tional review of case histories, struc-tured discussion sessions, and requiredterm papers.

I received a copy of the originalversion of STELLA the week beforeclasses began and reviewed it immedi-ately. It was obvious immediately thatSTELLA would allow me to teach thecourse as I wanted and within the con-straints (eg, limited student program-ming ability) that I faced. With sometrepidation, I cast aside my syllabus andoutlines and embarked on an adventureof experiential learning of my own as 1tried to stay ahead of the students.My goal was simple. I wanted the

students to examine and develop theiiunderstanding of how fish populationsrespond to environmental and humaninfluences and how humans adjust theirfishing behavior in response to thesame factors. Rather than learning sim-ply from ott rs' mistakes and succes-ses, they were challenged to state theirown understandings in the form ofmodels. They could then test the pre-dictions based on these principlesagainst the data of past fishery manage-ment practices. In the process of carry-ing out this exercise, they could refineand deepen their understanding of howfishery systems function, rather thansimply fmd that their ideas were corrector incorrect.

I began the course with an introduc-tion to the kinds of problems faced byfishery managers (relatively small databases and relatively poor understandingof how the systems function) and thenintroduced the basic methods of modeldevelopment and use. The first assign-ment was to develop 4 model of a cat-fish fishery in which fish entered thepopulation by stocking (no naturalreproduction) and were removed onlyby angler harvest or natural mortality.The class worked together to constructa conceptual model agreeable to every-one. They used data available in pub-lished literature and agency reports toestablish the quantitative relationships

between "converters" (determiningfactors) and "flow regulators" (proces-ses). In the absence of documenteddata, they relied upon data on relatedspecies and fisheries and expert advicefrom fishery managers.

Once the class had their quantitativemodel designed on paper, I gave them athirty minute introduction to the use ofSTELLA and then turned them loose ina laboratory with Macintosh computers.None of the students had used a Macin-tosh computer before. Within onehour, most had their model entered ontothe computer and operating.

My reason for describing this se-

quence is to highlight how easily andhow quickly students can learn to useSTELLA. They need some prior in-struction in the practice of modeling,some practice constructing andquantifying a simple model, and lessthan one hour of instruction to be ableto use STELLA. As with any program-ming language, students become moreaccomplished and can create morecomplex models with practice. Priorprogramming experience certainlyfacilitates a mom advanced approachinitially. Even so, students becomefunctionally literate in STELLAextremely quickly.

REDF

20

15I

SH

10

ST0 5CK

5 A(x 10 ) 0

I I

0

RE0FI

SH

ST0CK

20

Is

10

5

( x 10 )0

6 12

TIME (YR)18

REDFISH_STOCK vs TEMPERATURE

la17 10 18.05 19.00 19.95 20.90

TEMPERATURE (C)

24

FIGURE 5. Graphical output of the population model illustrated In Figure 3 using STELLA.Graphs can be presented as time series plots (A) or as scatter plots of two variables (B). Thefunction NORMAL was applied to converters 'Temperature" and "Plankton" to generate thevariation in (A).

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TABLE 1. Tabular output of data generated by a simulation run of the REDFISH modelusing STELLA. Conditions of variation for TEMPERATURE and PLANKTON are thesame as in Figure 5.

Time REDFISH S... TEMPERAT... PLANKTON HARVEST NATURAL ...0.0 5000.00 20.76 243.22 950.00 1900.00

1.00 249840.59 16.25 223.74 47469.71 94939.422.00 1008724.6 18.66 192.74 191657.67 383315.343.00 1194601.3 19.35 214.39 226974.25 453948.504.00 1221407.9 19.90 229.57 232067.48 464134.975.00 1249427.1 18.15 212.31 237391.16 474782.316.00 1247460.6 20.51 207.53 237017.52 474035.037.00 1231574.4 18.41 223.52 233999.14 467998.288.00 1230035.9 19.45 268.25 233706.83 467413.669.00 1242133.0 17.65 225.50 236005.27 472010.53

10.00 1209216.9 19.34 208.97 229751.19 459502.3811.00 1228582.4 19.41 242.13 233430.66 466861.3112.00 1207441.1 18.90 206.85 229413.81 458827.6313.00 1213822.5 18.51 286.60 230626.28 461252.5614.00 1210243.1 18.38 306.84 229946.19 459892.3815.00 1264946.0 17.00 194.24 240339.73 4806-'9.47

After the students had used their ini-tial model to ask some basic questionsabout stocking rates and harvest rates,they quickly found that their model wasextremely simple to use; in fact, it wastoo simple. In the second version of themodel, they incorporated inci'vidualgrowth, so that fish could be st,.-,-ked ata sub-harvestable size and would growinto the harvestable size class. Theycould !lave used a variety of modelstructures to accomplish this, but theysettled on breaking the single catfishstock of the original model into stocksof catfish of different sizes. Growthrates (ie, age of passage into the nextlarger size class) were determined byfeeding rates and by the total density ofall catfish. They increased thecomplexity of the outflow cid,: of themodel by incorporating lengthrestrictions on fish harvest such thateach size class had different harvestmortality rates.

Successive assignments built moredemanding features into the modelingproblem, yet all could be accomplishedwith relatively simple models. As withany modeling assignment, studentstended to make models more complexthan were necessary. STELLA became

unwieldy and slow as model complex-ity increased, serving as a negativereinforcement against excessive ,:om-plexity. To incorporate a more biolog-ical basis for fish somatic growth intopopulation models, the students de-signed a parallel model that used milli-meters of fish length as a stock thatcould be increased by environmentally-and dentisty-regulated growth equa-tions. By linking a series of theselength stocks, each representing an ageclass of fish, they court model fishgrowth with a von Bertalanffy model .5The students used the NORMAL func-tion in an equation for recruitment ofyoung fish into the adult stock to studythe effects of random variation in envi-ronmental conditions (lake level) on thesuccess of fish spawning and juvenilesurvival. The class developed all ofthese models as a group project whichallowed them to learn from each otheras well as from their own experiences.

For the final project however, I re-quired each student to develop his orher own research problem, a model touse in studying the research problem,and a report on his or her findings fromthe model. This assignment was themost difficult for the students, perhaps

because they had been too accustomedto the group dynamic in finding mod-eling tactics to accomplish their as-signed tasks. The fact that they hadbeen given the problem in previousassignments and had to define the prob-lem in the final assignment may havealso limited their success in thisexercise.

LIMITATIONS TO THE USE OFSTELLAIt should be obvious that I considerSTELLA an asset in my teaching. Al-though my initial use was for a gradu-ate course, it is quite adaptable to un-dergraduate courses. It is simpleenough that high school students (orever junior high students) could learnto use it, as long as they are given anappropriate introduction to the methodsof modeling. Yet it is powerful enoughto be useful in research applications.One of our Ph.D. graduates used STEL-LA to model the physiological responseof ruffed grouse to various winter habi-tat conditions. In spite of this breadthof usefulness, STF.LLA does havesome limitations.

Perhaps the first limitation is one thatsome may consider a trivial one. Edu-cational software that is usable only ona Macintosh computer is less accessiLlethan that which is produced for allmicrocomputer formats. STELLA is soclosely tied to the Macintosh interfacethat it sems unlikely that it will beavailable in PC or Apple H format. Weare fortunate. that our campus has rec-ognized the need for PC and Mac usesand has established computer labsequipped with both types of machines.Other educators may be more limited.This is a fault of educauonal planners,not of the software.

My only frustrations with STELLAhave come when I have tried to push itoeyond a pedagogical use. STELLAcan be quite useful in research applica-tions, however its major limitation is inthe site of models it can handle effi-ciently and its inability to link separatemod; s in joint or successive simulationruns.

Simulation time slows consid;rably asthe integration time steps are reduced

01988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENCE EDUCATION 0742-3233/88/S0.00 + 2.00


and as the number of stocks and flowsincreases beyond 8 or 10. Obviously,one solution is to keep models simple,yet some applications require moredetail to produce results usable in aresearch application. Another solutionis to break cumbersome models intosubunits that can be run in sequence.STELLA can be used in this way, but itrequires continuous involvement of theuser to move it from one step to thenext. It is not designed to allow theuser to automatically link these sub-units into a master run, feeding outputfrom one run as input to the next. Theonly way to accomplish this is to runthe first subunit in the series, save theoutput table in a file, import this datainto the next subunit as initial lata, runthe next subunit, feed the output to thesuccessive subunit, and repeat thesesteps for each subunit. In defense ofthe software, this use is extending thepackage beyond its designed use (toteach modeling), yet it is so close to be-ing useful in other research applicationsthat it seems a desirable and justifiableimprovement (perhaps as a "STELLA

for Research").One other limitation that concerned

me involved the random number gener-ating functions (RANDOM and NOR-MAL). These are extremely useful andefficiently designed functions built intothe software. Yet they suffer from aninconvenient limitation. When usingstochastic models, it is important tomake multiple runs of the model togenerate enough sets of output to deter-mine the statistical characteristics of themodel results. STELLA requires theuser to initiate each simulation rundirectly, rather than programming themachine to run the simulation 10 or 100times automatically and to save theappropriate output from each run.

Against the strengths of all thatSTELLA can do, particularly as ateaching tool, these complaints seemminor grousings. A more canny usermight be capable of programming themachine to accomplish what I cannot.If these uses of the software were thegoals of the designers, it should not benecessary to be a canny programmer.But the developers knew what they

wanted,3 and they succeeded remark-ably well in meeting their goals. I en-courage anyone interested in teachingwith simulations to try STELLA. Youwill not turn it away.

REFERENCES1. Eckblad, JW. Experimental data simu-

1-.ions in biology. Computers in Life,cience Education 4:60-63, 1987.

2. Grant, WE. Systems analysis and sim-ulation in wildlife and fisheries sciences.John Wiley & Sons, New York, NY,1986.

3. Richmond, B. STELLA:software forbringing system dynamics to the other98%. Proceedings of the InternationalConference of the System DynamicsSociety 1985:706, 1985.

4. Richmond, B, S Peterson, and P Vescuso.An academic user's guide to STELLA.High Performance Systems, Inc, Lyme,NH, 1987.

5. Ricker, WE. Computation and interpreta-tion of biological statistics of fishpopulations. Bulletin 191, FisheriesResearch Board of Canada, 1975.

ANNOUNCEMENT

A Physiology teaching software demonstration will take place at the XXXI International Congress ofPhysiological Sciences in Helsinki, Finland, July 9-14, 1989. Computers will be available on-site toallow participants a hands-on experience with the programs. Authors wishing to display their softwareare urged to submit abstracts describing the topic, educational goals, and hardware requirements of theirprograms. Copies of the programs and minimal documentation (operating instructions) must beforwarded to Helsinki by June 1, 1989. Information about the software demonstration can be obtainedfrom:

Information about the Congress andThe Secretariat, XXXI InternationalHelsinki, Finland.

Dr. Joel MichaelDepartment of Physiology

Rush Medical CollegeChicago, IL 60612

(312) 9 2-6426

the required registration material can be obtained from:Congress of Physiological Sciences, P.O. Box 722, SF-00101

0742-3233R840.00+ 100 1988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENCE EDUCATION""7,y


AIMS AND SCOPE The goal of Compu:.7.s in Life Science Education is to provide a means of communicationamong life science educators who anticipate or are currently employing the computer as aneducational tool. The range of content includes, but is not limited to, articles focusing oncomputer applications and their underlying philosophy, reports on faculty /studentexperiences with computers in teaching environments, and software/hardware reviews in







Reference style and form should co!low the "number system with ref:ences alphabetized"described in the Council of Biology Editors Style Manual. Reference) should be listLi inalphabetical order by the first author's last name, numbered consecutively, and cited in thetext by these numbers.


Authors are responsible for accuracy of statements and opinions express,:d in articles. Allauthors submitting manuscripts will be sent a copyright transfer form to complete. Thecompleted form must be returned before the work will be published.

SUBSCRIPTION INFORMATION Computers in Life Science Education is published monthly by National Resource forComputers in Life Science Education, Mail Stop RC-70, University of Washington, Seattle,WA 98195. Subscription rate is $30.00 for 12 issues, including postage and handling in theUnited States And Canada. Add S20.00 for postage (airmail) in Mexico and Europe and523.00 for the rest of the world.

This newsletter has been register( with the Copyright Clearance Center, Inc. Consent isgiven for copying of articles for personal or internal use, or for the personal or internal use ofspecific clients. This consent is given on the condition thr.t the copierpay through the Centerthe per-copy fee stated in the code on the first page for copying beyond that permitted by theUS Copyright Law. If no code appears on an article, the author has not given broad consent'o copy and permission to copy must be obtained directly from the author. This consent doesnot extend to other kinds of copying, such as for general distribution, resale, advertising andpit -notional purposes, or for creating new collective works.

Address orders, changes of address, and claims for missing issues to NRCLSE, Mail StopRC-70, University of Washington, Seattle, WA 98195. Claims for missing issues can behonored only up to three months for domestic addresses and six months fo foreignaddresses. Duplicate copies not be sent to replace ones undelivered due to failure tonotify NRCLSE of change of address.


POSTMASTER: ;,end address changes to Computers in Life Science Education, NRCLSE,Mail Stop RC-70, University of Washirt!ton, Seattle, WA 98195.

1988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENCE EDUCATION 0742-3233/8S1$0.00 + 2.00

7

VOLUIV'T S NUMBER 10, OCTOBER 1988 CLSEE3 5(10), 73- 80,1988 ISSN 0742-3233

Ar p9: 1110111111111111101111

HAROLD I. MODELLDepartment of RadiologyUniversity of WashingtonSeattle. Washington


THEODORE J. CROVELLOGradute Studies and ResearchCalifornia State University, Los AngelesLos Angeles, California


TSEGAYB HABTEMARIAMSchool of Veterinary MedicineTuskegee UniversityTuskelice, Alabama

DONNA LARSONSchool of NursingGrand Valley State 2ollegeAllendale. Michigan


JAMES E. RANDAU.Department of PhysiolorIndiana UniversityBloomington, Indiana

PATRICIA SCHWIRIANCollege of Nursing

State UniversityColumbus. Ohio

RICHARD STULLCollege of PharmacyUniversity of Arkansas:Little Rock, Atkansas




GLEN YOSHIDADepartment of Life SciencesLos Angeles Southwest Collegelin Angeles, California

NRCLSE

CONTENTS

NRCLSE BEGINS SOFTWARE EVALUATIONPROGRAM

Harold I. Modell

A STUDENT'S VIEW OF COMPUTER-BASEDCURRICULUM INNOVATIONS

Susan Erdman


NRCLSE SOFTWARE EVALUATIONSUBMISSION FORM

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NRCLSE BEGINS SOFTWAREEVALUATION PROGRAMHarold I. ModellDepartment of Radiology, University of Washington, Seattle, Washington

One of the goals of NRCLSE has beento establish a peer critique mechanismfor software related to life science edu-cation.4 Over the past two years. broadissues related to the overall focus ofsuch a program have been highlightedin CLSE, and reader input was encour-aged to help define evaluation crite-ria's The underlying issues, however,appear, to be too complex to definesimpit, criteria.

Recently, EDUCOM has also died toaddress these issues through two eial-uatiun efforts. The National Center forResearch to Improve PostsecondaryTeaching and Learning (NCRIPTAL)established an annual higher educationsoftware awards program, and EDU-COM initiated a scholarly review ofacademic software project. In its firstyear, the latter has focused on com-puter based learning modules in three

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74 COMPUTERS IN LIFE SCIENCE EDUCATION, VOLUME 5, NUMBER 10, OCTOBER 1988

general curricular areas, only one ofwhich is life science related. Theseprograms tend to illustrate aspects ofthe dilemma facing any evaluationprocess in this area.

Entries in the NCRIPTAL programundergo evaluation by instructionaldesign experts as well as content ex-perts. As a result, finalists in the com-petition tend to be at the "cutting edge"in terms of instructional design criteriaand utilization of available computercapability. Thus, although some oldersoftware may be very effective in pre-senting material and hence potentiallyvery helpful to educators, they may fallshort of ratings that would convey thisto potential users.

The guidelines for reviewers in EDU-COM's software review project reflectan assumption that the software hasbeen designed primarily to "teach" aparticular topic. Thus, programs thathave been designed to augment class-room interaction or provide animatedillustraeon materi in a group setting12may not receive appropriate reviews.

Two approaches to the overall prob-lem may be taken. The first is to delayimplementing a critique mechanismuntil clear criteria are evident. Thesecond is to implement a mechanismthat will not be ideal but that will pro-vide a framework in which evaluationcriteria may be tested and allowed toevolve as more experience is gained.After taking the first approach forseveral years, it has become clear toNRCLSE that significant nrogress willnot occur until the latter approach isattempted.

THE NRCLSE PEER CRITIQUEPROGRAMThe NRCLSE peer critique programbeing initiated with this month's issueof Computers in Life Science Educa-tion will serve as the first step in thisprocess. Its initial focus will be micro-computer software (Apple II, MS-DOS,and Macintosh), and its goal will be

two-fold: to provide software authorswith feedback and to provide userswith information. The feedback willinclude a critique of the software andsugges'ions for ways to make it moreversatiL. Each piece of software willbe critiqued from content and instruc-tional design standpoints. Becausecomputer memory and mass storage areno longer limiting factors as they wereseveral years ago, program efficiencydoes not seem to be as important anissue as it once was. Thus, the pro-grams will not be examined from acomputer programming standpoint.

Each author will be required to sub-mit a written narrative addressing thefollowing questions to give reviewers abasis for initial evaluation.

What need prompted the develop-ment of the software?

For what student population was thesoftware intended?

In what environment is the softwareintended to be used (eg, groupdiscussion, independent study)?

Why was the specific format of thesoftware chosen? (ie, What is theunderlying philosophy of thesoftware?)

How long has the software beenused with students?

What attempts have been made toevaluate the impact of the soft-ware, and what were the results ofthose evaluations?

The software (copies supplied by theauthor) alork, with copies of the narra-tive will be sent to at least two contentreviewers and one reviewer with exper-tise in instructional design. The soft-ware will be returned to the author withthe reviewers' comments unless theauthor designates that reviewers maykeep the software.

FOCUS OF EVALUATIONS

Although development of specific eval-uation criteria will require some experi-

ence with the critique process, guide-lines, in the form of a series of ques-tions, will be provided for the review-ers. The content reviewers will beasked to focus initially on the followingquestions.

Is the content accurate?Does the software appear to meet

the design criteria specified by theauthor?

Are error messages provided whereappropriate?

Do the form of ',Tor messages (ifappropriate) provide an additionallearning experience for students?

Is the software suitable for studentpopulations or educational settingsother than those specified by theauthor? If not, what modificationswould be necessary to make thesoftware more versatile?

Are you aware of other software thatcovers the same material? If so,who are the authors, and how doesthis program differ fromothers?

What features did you like bestabout the program?

What features did you like leastabout the program?

the

Reviewers with expertise in instruc-tional design will be asked to examinethe program with the following ques-tions in mind.

Are the input and output schemesfollowed in this program consistentwith sound instructional designprinciples (not necessarily "scam ofthe art")?

How could the input and outputscreens be improved and still meetthe program needs as stated by theauthor?

FUNDING FOR THE PROGRAMBecause NRCLSE has been unable toidentify potential sources of fundingfor such a program, the program must

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COMPUTERS IN LIFE SCIENCE EDUCATION, VOLUME 5, NUMBER 10, OCTOBER 1988 75

be self funding. Each author will berequired to submit a nominal fee of $25to cover handling, mailing, and follow-up costs associated with the reviewprocess.

SOFTWARE REPORTSTo meet the goal of providing softwareusers with information concerningavailable software, each complete pro-gram received will be included in theCLSE Where's the Software listing. Inaddition, each complete package will

be considered for an in-depth reviewarticle in CLSE.

REFERENCES1. Heckman, J, MB Wang, M Harris, M

Gordon and A Cardell. The use ofcomputer animations during lecturesin physiology. Computers in LifeScience Education 4:73-76, 1987.

2. Levine, SD. Microcomputer graphicsas a substitute for lecture slides.Computers in Life ScienceEducation 1:33-36, 1984.

3. Michael, JA. Letter to the editor:comment on peer review or peercritique of software. Computers inLife Science Education 4:91-92,1987.

4. Modell, HI. Establishment of thenational resource for computers inlife science education. Computers inLife Science Education 3:1-5, 1985.

5. Modell, HI. How shall we evaluatesoftware peer review or peercritique? Computers in Life ScienceEducation 4:68-69, 1987.

A STUDENT'S VIEW OF COMPUTER-BASEDCURRICULUM INNOVATIONSSusan ErdmanDivision of Comparative Medicine, Massachusetts Institute of Technology, Cambridge,Massachusetts

Unlike the classes before them, eachstudent of the Class of '88 of Missis-sippi State University College of Vet-erinary Medicine was required to pur-chase a computer upon matriculation'Having been Ane of these students, Ihad the opportunity to witness, firsthand, computer-based innovations inthe curriculum. After four years, someof -,:s fantasized about nationwide com-puterized networking systems whileothers imagined a 128K Macintoshcomputer falling twenty stories andcrashing to the concrete while DavidLetterman's audience cheered. Nodoubt, the Class of '88 were pioneers,and, no doubt, the introduction of com-puters into the curriculum was progres-sive. The progress, however, was notwithout controversy.

The computers were added to our cur-riculum primarily to facilitate informa-tion management. The computer appli-cations in the curriculum included

word-processing, &agnostic toois, dataorganization aids, spreadsheets, andgraphics. MacWrite data disks re-placed the traditional hard-copy sopho-more and junior course objectives.PKC diagnostic and management soft-ware was briefly introduced. Think-tank was applied to organize projects,and Jazz was applied to hypotheticalpractice management exercises. Por-tions of our freshman anatomy courseutilized Filevision to computerize les-sons so that information leaves coordi-nated with anatomical illustrationspresented logically on the computerscreen.2 Today's classes also use com-puterized case analyses that unfolduniquely with each management deci-sion throughout a hypothetical case.

As freshmen, most of us sat staringblankly at the computer screens. Mostof us had little previous computer ex-perience, and some had no interest inlearning. Fortunately, the curriculum

planners chose Macintosh computers,which are generally easy to learn touse. This standardization alFo createdcomputer compatibility that facilitatedexchange of information and skills.Word processing with MacWrite quick-ly became popular for vocational andavocational computer use from editingcourse objectives to polishing resumes.Only computer games were used morefrequently than MacWrite. The volu-minous course objectives that were pre-sented on a computer disk were easierto store (for example, in a pocket) andaccess than hard-copies. Moreover,students now had the option of person-alizing the course outlines for futurereference. (Course objectives are cur-rently presented with Hypercard, whichfurther increases flexibility and acces-sibility of information storage by estab-lishing software links among relateddata.) Although we had few opportu-nities to apply diagnostic software in

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COMPUTERS IN LIFE SCIENCE EDUCATION, VOLUME 5, NUMBER 10, OCTOBER 1988

the didactic portion of the curriculum,s,:ne students applied diagnostic soft-ware to clinical cases during the senioryear. On several occasions, computer-generated differential diagnoses dem-onstrated their utility in the clinics.

Interestingly, on a more negativenote, my classmates frequently chose tostudy and work from hard-copies in-stead of from the computer screen, per-haps because they were more comfort-able with the familiar format or, per-haps, because computer screens wereharder on the eyes during long hours ofstudy. Even more serious, opponentsof the computerization suggested thatcomputers might compromise masteryof the "veterinary essentials."

Considering the burgeoning advanceswithin the veterinary disciplines, itseems difficult to construct an appro-priate and manageable knowledge baseusing traditional approaches. -Yet, it isimportant that veterinarians have ac-cess to pertinent and current informa-tion. Our veterinary curriculum dem-onstrated that computers facilitate ac-cess to a dynamic information sourcethat can grow with the field. Indeed,computers can help veterinariansharness some of our expanding know-lee.e resources to improve futureveterinary care.

Who knows how much professionalimpact our computer experience had onthe Class of '88? When the future

demonstrates ',flat computers improveveterinary services, no doubt, com-puters will be further integrated andutilized within our profession. TheClass of '88 and their successors willbe well prepared.

REFERENCES1. Bushby, PA. Computerized veteri-

nary medical information systems:impact on veterinary medicaleducation. Computers in LifeScience Education 3:33-36, 1986.

2. Westmorland, N. A graphicsoriented database for anatomy andphysiology. Computers in LifeScience Education 4:54-56, 1987.

KEEPING ABREAST OF THE LITERATUREThe following citations are presentedas part of a quarterly feature in CLSEdesigned to help readers become awareof current literature pertinent to com-puter applications in life science educa-tion. The feature did not appear i.:1 theSeptember issue as regularly scheduledbecause of snace limitations.

Bagnall RD et al: Computerisedteaching of temporomandibular jointdysfunction. Br Dent J 164(4):98,1988.

Dooling SL: Designing computersimulations. Comput Nurs5(6):219-224, 1987.

Duchastel PC: Display and interactionfeatu:cs of instructional texts andcomputers. Brit J Educ Tech19(1):58-65, 1988.

Eiser L: What makes a good tutorial?Classroom Computer Learning8(4):44-47,50-51, 1988.

Gaston S: Knowledge, re.ention, andattitude effects of computer-assistedinstruction. J Nurs Educ 27(1):30-34, 1988.

Hanes DP et al: Computer-assisted

-..struction in pharmacokinetics forsecond-year students. I Med Educ63(4):336-338, 1988.

Hebda T: A profile of the use ofcomputer assisted instruction withinbaccalaureate nursing education.Comput Nurs 6(1):22-29, 1988.

Kordas M et al: Personal computers inteaching quantitative aspects inundergraduate physioilogy. MedEduc 21(6):468-470, 1987.

New integrated video and graphicstechnology: Digital video inter-active. Optical Information Systems7(6"12-415, 1987.

Persp. Aives in CD-ROM for informa-tion storage and retrieval. Lunin LFand Schipma RB, Eds. Journal ofthe American Society for Informa-tion Science 39(1):30-66, 1988.

Siegel JD et al: Computerized diag-nosis: Implications for clinicaleducation. Med Educ 22(1):47-54,1988.

Simon W: Anatomy helper: Computer-assisted anatomy instruction.Comput Methods Programs Biomed26(1):71-74, 1988.

SimpE-n D et al: Application of aframe-based computer-aidedlearning system in clinical chem-istry. Med Educ 21(5):399-404,1987.

Smith DA: Does computer assistedinstruction work? Pa Med 91(3):8,1988.

Swanson DB et al: Assessment ofclinical competence: Written andcomputer-based simulations.Assessment and Evaluation inHigher Education 12(3):220-246,1987.

Thomas SB: Microcomputer telecom-munications: Basic principles forhealth education research. HealthEducation 18(6):16-19, Dec-Jan,1987-88.

Whiteside MF, Whiteside JA: Micro-computer authoring systems:Valuable tools for health educators.Health Education 18(6):4-6, Dec-Jan, 1987-88.

Wiist WH: Update on computer-assisted video instruction in thehealth sciences. Health Education18(6):8-12, Dec-Jan, 1987-88.

©1988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENCE EDUC ATION 0742-3233/8840.00 + 2.110


NRCLSE SOFTWARE EVALUATIONSUBMISSION FORMThe NRCLSE software evaluation program has been initiated to promote development of high quality, versatile educationalsoftware in the life sciences by providing authors with feedback from critiques by life science educators and instructionaldesigners. To ensure that software is reviewed in an appropriate fashion, it is essential that reviewers fully understand therationale underlying the design criteria chosen by the author and the environment for which the software is intended. Pleaseprovide the following information concerning each software package to be evaluated.

Submit 3 complete copies of the software and all supporting documentation along with $25 (to cover handling, mailing,and follow-up costs) to Software Evaluation Program, NRCLSE, Mail Stop RC-70, University of Washington, Seattle,WA 98195.

Author's name:

Author's address:

Title of software: Content arm:

Minimum hardware requirements:

Optimal hardware configuration:

Software requirements (operating system, etc):

Student population for which software was written:

Environment for which software is primarily intended (independent study, classroom discussion, lecture enhancement, etc):

7How long has this software been used by students?

Can reviewers keep the review copy of this software?

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78 COMPUTERS IN LIFE SCIENCE EDUCATION, VOLUME 5, NUMBER 10, OCTOBER 1988

Describe the underlying philosophy of this software. What need prompted the development of this software? Why was thespecific format of the software chosen? What goals did the input/ouput scheme (or screen design) address? Is documentation anintegral part of the package? How is it intented to be used? (Use additional pages if necessary.)

©1988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENC' EDUCATION 0742-n33/88/50.00 + 200

10


Has the software been evaluated by students?

Briefly describe the format and results of any student evaluation.

What attempts have been made to evaluate the impact of the software on student progress?

What were the results of the impact evaluation?

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80 COMPUTERS IN LIFE SCIENCE EDUCATION, VOLUME 5, NUMBER10, OCTOBER 1988

N.M=11AIMS AND SCOPE The goal of Computers in Life Science Education is to provide a means of communication

among life science educators who anticipate or are currently employing the computer as aneducational tool. The range of content include but is not limited to, articles focusing oncomputer applications and their underlying pi .tosophy, reports on faculty/studentexperiences with computers in teaching environments, and software/hardware reviews in


INVITATION TO CONTRIBUTORS Articles consistent with the goals ofComputers in Life Science Education are invited forpossible publication in the newsletter.







Authors are responsible for accuracy of statement:, and opinions expressed in articles. Allauthors submitting manuscripts will be sent a copyright transfer form to complete. Thecompleted form must be returned before the work will be published.

SUBSCRIPTION INFORMATION Computers in Life Science Education is published monthly by National Resource forComputers in Life Science Education, Mail Stop RC-70, University of Washington, Seattle,WA 98195. Subscription rate is S30.00 for 12 issues, including postage and handling in theUnited States and Canada. Add S20.00 for postage (airmail) in Mexico and Europe andS23.00 for the rest of the world.

This newsletter has been registered with t! a Copyright Clearance Center, Inc. Consent isgiven for copying of articles for personal or internal use, or for the personal or internal use ofspecific clients. This consent is given on the condition that the copierpay through the Centerthe per-copy fee stated in the code on the first page for copying beyond that permitted by theUS Copyright Law. If no code appears on an article, the author has not given broad consentto copy and permission to copy must be obtained directly from the author. This consent doesnot extend to other kinds of copying, such as for general distribution, resale, advertising andpromotional purposes, or for creating new collective works.

Address orders, changes of address, and claims for missing issues to NRCLSE, Mail StopRC-70, University of Washington, Seattle, WA 98195. Claims for missing issues can behonored only up to three months for domestic addresses and six months for foreignaddresses. Duplicate copies will not be sent to replace ones undelivered due to failuretonotify NRCLSE of change of address.


POSTMASTER: Send address changes to Computers in Life Science Education, NRCLSE,Mail Stor RC-70, University of Washington, Seattle, WA 98195.

©1988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENCE EDUCATION 0742-3233/88/S0.00 + 25;

VOLUME 5, NUMDER 11, NOVEMBER 1988

HAROLD I. MODELLDepartment aRadiologyUniversity td WashingtonSeattle, Washington

MARCEL BLANCHAERDepartment of BiochanistryFaculty of MedicineUniversity of ManitobaWinnipeg. Manitoba

THEODORE J. CROVELLOGraduate Studies and ResearchCalifornia State University. Los AngersLos Angeles, California


TSEGAYE IIABTEMARIAMSchool of Veterinary MedicineTuskegce UniversityTuskegce, Alabama

DONNA LARSONSchool of NursingGrand Valley State CollegeAllaulale, Michigan

TEROY M. MIIUTENGraduate School ("Biomedical SciencesUniversity of Texas Health Science CenterSan Antonin, Texas

JAMES E. RANDALLDepartman of PhysiologyIndiana UniversityBloomington. Indiana






GLEN YOSIIIDADepartment of life SciencesLos Angeles Southwest CollegeLos Angeles. California

NRCLSF

INNS IN..

CLSE.E3 5(11), 81-88, 198E ISSN 0742 -3233

I t r I 11111

11.101111111

CONTENTS

SELECTING AN AUTHORING SYSTEM 1988:HOW THE VENDORS MAKE IT MORE DIFFICULT 81

Mary S. Trainor and P. Alexandra Schltejann

FROM CAI TO ICAI:TWO EXAMPLES ALONG THE WAY

Joel A. Michael, Allen A. Rovick, Martha Evens,Nahkoon Kiln and Mol Hague

86

SELECTING AN AUTHORINGSYSTEM 1988: HOW THEVENDORS MAKE IT MOREDIFFICULTMary S. Trainor and P. Alexandra SchultejannLos Alamos Nat:anal Laboratory, Los Ala: vs, New Mexico

Selecting an authoring system can beail intimidating prospect, consideringthe number and diversity of systems onthe market today. Most organizationscontemplating an authoring system pur-chase have neither the time nor persc n-nel to devote to lengthly benchmark

testing.5.8.9 Instead, these organizationsare forced to make an evaluation ofnumerous authoring systems basedsolely on Vie information and demon-stration ix ckages provided by the ven-dors. Tha major problem posed by thisapproach is that these packages do not

Editor's Note

The papers in this month's CLSE wire presented at the 30th InternationalConference of the Association for the Development of Computer-BasedInstructional Systems held in Philadelphia, PA, November 7-10, 1988 and arereprinted from the proceedings of that conference.

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include the information necessary toevaluate the usefulness and applica-bility of an authoring system for theorganization's purposes.

The Cognitive Systems EngineeringGroup at Los Alamos National Labora-tory has been active in the computer-based training (CBT) field for morethan three years. We have developedseveral prototype computer-based sys-tems for various agencies and are wellacquainted with the constrains of pro-gramming languages versus authoringsystems.

This paper describes our experiencesin attempting to recommend an au-thoring system that would hest suit thepurposes of our client. Our client hadvery specific requirements, and wewere limited by both time and re-sources in the evaluation process. Weencountered numerous difficulties inour attempt to conduct a thoroughevaluation under our time and fundingconstraints. The information anddemonstration packages provided bythe vendors contained insufficient in-formation for us to thoroughly evaluatethe authoring systems. We presentsome suggestions to vendors on theinformation that should be provided toallow potential customers adequatedata for evaluation. In addition, thissame information can be used by po-tential authoring systems customers (egtrainingieducation managers and de-signers) in their evaluation of authoringsystems. The goals of the paper are

1) to suggest to vendors that theyprovide more consistent andthorough information, and

2) to encourage potential customers tobe more demanding of the vendorsand more educated in theirevaluations.

METHODSSelecting an authoring system shouldbe done with some care to ensure thatthe system is compatibL with the cus-tomer's needs and desires. We dividedthe selection process into four majorsteps:

Step 1. Define requirementsIL is important to define the needs of the

group that will be developing the CBT,both now and in the future. Theserequirements not only include suchfactors as the skills of the person(s)who are going to be doing the authoringbut also the desired features of thecourseware. A tradeoff analysis needsto be done, determining priorities, forin the development of each authoringsystem, priorities were set. Yourpriorities should 'latch those of thesystem developers. For example, it isdifficult to obtain an easy-to-learninterface which also allows for sophis-ticated instructional strategy develop-ment (eg, simulation). It also needs tobe taken into account that as a CBTprogram grows, the level of sophisti-cation desired increases. Thus, featureswhich seem unnecessary now (eg,ability to jump out to a programminglanguage, digital audio) may becomehighly desirable a year from now.Unless these features are anticipatedselection of a highly cons...miningsystem may occur. The specific needsand requirements for the evaluation weundertook were:

An authoring system that is compat-ible with Zenith Z248's forauthoring and the EIDS system fordelivery.

A system capable of handling a vari-ety of instructional strategies, in-cluding drill and practice, tutorial,gaming and simulation.

A computer-managed instruction(CMI) capability which allows forsuch features as customized recordkeeping, report generation, a varietyof test modes, and learning prescrip-tion generation.

The capability to incorporate othergraphics and routines developed in aprogramming language into theauthored courseware. This wasdesired because of future flexibility,allowing for addition of features notpossible through the authoringsystem.

"A system that is easy-to-learn fornonprogrammers with minimal timespent on documentation.

' Versatility so that authors who be-come familial with the system willbe able to customize the interface to

their own preferences.The capability to customize thefeedback relative to the course andto include sophisticated answer-judging routines.

"A system that provides a variety ofquestioning formats.

A system that does not charge royal-ties or include a separate presenta-tion system.

' Minimum maintenance costs.The ability to add videodisc capabil-ity, and perhaps CD-ROM, in thefuture.

Step 2 Select authoring systems for in-depth evaluationBecause of the number of authoringsystems on the market, it was not pos-sible to evaluate them all. We startedthe narrowing down process by doingbackground research on authoringsyste.- 1-4.6.730." and making a prelim-inar, ..election from the CBT Guide toComputer-Based Training Systems andCourseware published by Data Train-ing and Weingarten Publications, Inc.12The Guide covers the basic atiributes of98 authoring systems, and Data Train-ing provides a matrix of system fea-tures versus authoring system. TheGuide, literature, and our own experi-ence allowed us to select eleven sys-tems as candidates for evaluation.

Step 3. Contact vendors for informationand demonstration packetsAll eleven vendors were contacted byphone for information about their prod-ucts ar. for a demonstration of the sys-tem. Four vendors were not able tosend demonstration discs for evaluationbecause they had not produced rile.One vendor subsequently sent the en-tire authoring system for us to preview,but we did not have the 8 to 12 hoursrequired to adequately evaluate thesystem. Of those having a demonstra-'ion disc, most desired a small paymentfor the disc ($15-25). We explainedthat we felt, since this was a mainmarketing tool of theirs and we werepotential customers, we should be pro-vided the disc free of charge. In addi-tion, in our institution it would havecost far more than $15 to $25 to get thenecessary purchasing paperwork

©1988 BY NATIONAL RESOURCE FOR COMPUTERS LN LIFE SCIENCE EDUCATION 0742-3233/8840.00+ 2.00

COMPUTERS IN LIFE SCIENCE EDUCATION, VOLUME 5, NUMBER 11, NOVEMBER 1988 83

through the system (not to mentiontime). All vendors cooperated, but inseveral instances, many phone callswere required in order to obtain thedisc. We had to describe in detail whowe were and the nature of the evalua-tion.

The printed information we receivedon the authoring systems consisted pri-mariiy of sales literature; ma ty vendorsdid not send the pertinent technical de-tails we required for a thorough evalua-tion. When queried on the phone aboutthis, several vendors said that we weren.ore inquiring than most of their po-tential customers, andthus they did oothave literature prepared at that level oftechnical detail.

These factors contributed to this beinga most time consuming and unpleasantstep in the evaluation process.

Step 4. In-depth evaluation of theauthoring systemsThe next step was to analyze each au-thoring system relative to our criteriaand based upon the information anddemonstration packages received. Ourcriteria, shown in Table 1, were devel-oped from our experience, the litera-ture, and from the evaluation require-ments. We had thought that the DataTraining Guide would provide us thenecessary criteria, but after reading itthrough, we discovered that Data Train-ing only set the features list, and thevendors had great liberty in what theysubmitted under each feature. None ofthe information was parallel, makingromparisons difficult, it not impossible.We are not faulting Data Training inthis discussion, as they were providinga very useful data clearinghousefunction compiling the system/vendorinformation.

Step 5. Recommendation of an authoringsystem for purchaseThe last step was to examine our eval-uation information and make a finalselection. Unfortunately, our resultswere quite inconclusive because ofinadequate information (see discussionof this under RESULTS below). Wewere able to narrow final candidates tothree, however. Because we needed tomake a recommendation, we selected

Table 1. Questions to ask when choosing an authoring system

1. What Is the cost per instructor station? per student station? What kind of agreementdo I need? If number of student stations is large and undefined at this point, then anagreement for unlimited usage and no special presentation system is probably desired.

2. What Is the royalty agreement? Am I willing to pay the vendor for each externa.usage of my courseware? If unlimited distribution is 'esirP4, ask about cost of this upfront.

3. How easy-to-learn and easy-to-use Is the system? Many vendors say their system iseasy, and yet they require extensive training delivered by them. Call current systemusersand ask them and/or try the system out for yourself to answer this question.

4. What are the hardware requirements? Does the system require some specializedhardware you do not have access to or cannot afford? Some vendors developed their systemto run on one set of hardware and then retrofit for others. If your hardware is retrofitted, itmay be awkwn,-A

5. What support does the vendor supply? Does the vendor supply telephone consulting(you will need it as you get started)? Are software updates provided? Are the updatesdesigned to be compatible with earlier versions?

6. What graphics, video, input devices and audio are supported? The power of thetechnology comes through in the use of high quality graphics (including overlay on video),digital audio (not just audio on videodisc), the ability to use touch or mouse as well askeyboard, and the videodisc You may not want these now, but you probably will later.Don't bind yourself to an authoring system which is constrained and not keeping up withnew technologies.

7. Which instructional strategies are supported? Many authoring systems constrain youto tutorial and drill and practice formats, yet the simulation and gaming strategies may beneeded for a particular applicaion. How does the vendor define a particular strategy? Thevendor may define it differently titan you do as well.

8. How does the computer-mtnaged Instruction (CM) work? Most systems have CMI,but it may not match your needs. What record keeping and reporting do you require? Willthe system require a central file server? Do you have to pay extra for CMI?

9. What response and questigning strategies can be used? You will want to be able to asksome open-ended questions, as well as multiple choice questions. Wal the system allow forthis? Does it have a built-in pelting corrector? Can several specific hints be given fordifferent wrong answers, to aid the student in reasoning to the correct answer?

10. How flexible Is the interface for different screen designs? Can you have differenttypes of m ...us: located at different screen locations? of different sits? with icons/graphics?

11. Does the system support team development? Can one person be working on graphicswhile another working on audio and text for thesame lesson at the same time?

12. How easy Is to change and update a lesson? Maintenance costs can be high andmuintenance is always necessary. What features does the system have to accommodatemaintenance?

13. Can it talk V: programming languages? If an authoring system has an efficient way tojump out to code written in a programming language, then it provides much greater flexibil-ity than a system which does not have this capability. This is desirable whether you need thisflexibility now or not.

le. Can it import courseware written using other systems? You may need to author orwrite scripts using other software systems, and if you can import those files into theauthoring system directly, you can save a great deal of time.

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one of the fmal three systk s/vendorson the basis of tne degree of coopera-tion they gave us on the phone, theirreputation, and the apparent expansibil-ity of the system.

RESULTSSome of our evaluation criteria werereadily answerable either through thedemonstration packages or in the in-formation sheets (numbers 1,24,8,16).Compatibilities and system featureswere fairly easily determined. In fact,the authoring systems we were evaluat-ing were remarkably similar in theirfeatucs with little apparent different:ehto use or discrimination purposes.

In the area of cost, it became apparentthat the advertised cost was very nego-tiable depending upon the size and na-ture of the contract, as well as the busi-ness of the contracting institution (eg,education or government). For exam-ple, if an institution were to purchaseseveral authoring workstations andmany student presentation systems, theprice would be reduced significantly.One could only obtain this type ofinformation on the phone throughprobing, and therefore many potentialcustomers would net obtain this infor-mation. Another cos: issue resulting inconfusion was royalties. an our case,we did not want to have to pay royaltieson intee.ally developed and distributedcourseware, but rether have the free-dom of distribution. kleveral vendorsseemed ill-prepared to cone with thisrequirement in terms of cost estimate,even though it seems to be a con onlyheld and desirable requirement in thecommunity.

We were unable to make any judge-ment on several key criteria because ofinadequate information. These criteriaare listed and described le low.

Ease-of-useOnly two of the demonstration pack-ages included enough of the program sothat a limited course could be built, giv-ing us a true "hands-on" experiencewith the authoring system. Withoutactually trying to use the authoring sys-tem, it was impossible to evaluate ease-of-use. Similarly, without an exampleof documentation, we were unable . to

evaluate it or how much the programdepended upon it. Vic discussed thisdrawback of the demonstration discswith se' eral vendors, and they agreedwith our criticism. The vendor claimedthat most people evaluating the systemsdo not request evaluating case-of-useand ease-of-learning (even though theseare very relative) -id that they wouldnot be 2roducing demo discs to makethis possible. The only alternative theysuggested was trying to use the com-plete authoring system package (someoffered to send one on a trial T,.asis), yetsuch use requires a larger time invest-ment than we (and most potential cus-tomers) have for evaluation.

External interfaceWe were unable to determine the extentthe authoring system was capable ofinterfacing with other software andprogramming languages. Although allof the systems we were evaluatingclaimed this capability, it became ap-parent through review ..;is the literaturethat some systems were more capablethan others. In one case, for example,jumping out to some external code wasquite easy, but quickly jumping backinto the authoring system coursewarewas not poss:11.

Computer managed Instruction (CMI)Only three of the systems evaluatedincluded sufficient information on theCMI capabilities. The others informedyou that they did have the capability oftesting and record keeping, but did notprovide intormation on the nature ofthe CMI interface, the number andtypes of records kept routinely, themethod of access for students versusinstructors, the learning prescriptionfeature availability, or the types oftesting possible.

Question formatOnly two of the systems evaluated ais-cussed the su'aject of possible questionformats adequately and, then, only onthe demonstration disc. Most of thesystems mentioned multiple choice andfill-in as questioning modes, and someincluded matching and true/false. Formost of the systems, we could not de-termine if short answer or long answer

questions were possible. In addition,there are a variety of ways of designingquestioning screens, and yet we had noway of knowing from the demonstra-tion discs which types of interfaceswere available for which systems andhow easy they were to generate.

Customized feedbackWe v, ere able to determine the extent efcustomized feedback and answer judg-ing only from the :wo demonstrationdiscs that allowed us to actually build aprogram. These discs represented awide range in feedback treatments Pos-sible, thus the complexity of the crite-rion was evident. We were interesLxlin information such as h, long thefeedback could be, how many custom-ized responses could be programmedper course, and how the answer judgingwas handled. This parameter was im-portant to us, because we wanted to aidour students in reasoning to the correctanswer for a problem instead of justbeing told "try again" or being told theright answer after only two incorrectansr.ers. The majority of systems weevaluated did not fully discuss the sub-ject, thus our evaluation was incom-plete for this criterion.

Instructional strategiesInstructional strategy, or the methodused to aid the student in achieving theobjectives, is critical to a good matchbetween the subject matter and the stu-dent needs. For example, a drill andpractice strategy is adequate for learn-ing multiplication tables but is veryinadequate in assessing a student'sability to operate a particular controlpanel. Since most installations need toprovide courseware on a variety of ap-plications requiring a variety L. instru..-tional strategies, flexibility and robust-ness here was necessary. None of thesyste,us we evaluated adequatelytreated the instructional strategies issue.It was also not possible to deduce thisinformation based on the data providedin the information and dcmonstrationpackages.

CompilationOne of the systems indicated that theresultant courseware was an uncom-

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piled program written in a program-ming language. The other seven pro-,'ucts we evaluated did not indicate if-te courseware generated by the author-ing language was compiled or co :e.

DISCUSSION ANDCONCLUSIONSThe information provided by the ven-dors on their authoring systems was notadequate to make a thorough evaluationof the products. We found that mast ofthe information provided by the yen-dm was sales- and feature-orientedrather than oriented toward the per-formance of the system and the modesof instruction it supports. Most of thedemonstration discs we looked at werecomputer-based sales pitches that dem-onstrated samples of courses and graph-ics created by the authoring system.They did not indicate how the programwas achieved and the amount of timeone could expect to spend generating asimilar course, nor did they indicate thetypes of instructional designs that couldbe generated beyona a linear set ofquestions and answers.

When questioned by us, the vendorsagreed to the limitations of the informa-tion packages and demonstration discs.It seems, however, that we as custom-ers are not demanding that they pro-duce alternatives. Potential customersof the authoring systems need to bemore thorough in their investigationsand evaluations and more demanding ofthe vendors' mat:rials. Similarly, ven-dors need to lend greater assistance totheir prospectiv& customers by provid-ing the necessary information so thatusers can make an intelligent selection.Based on our experience, we recom-mend that potential customers generatea list of criteria prio. evaluation andbe firm with the vendors in obtaininganswers. We recomr nd that vendorsinclude more detailed technical infor-mation in their sales and demonstrationpackages. the'specifics of the addi-tional informatior needed is describedbelow.

1. Our foremost recommendation isfor the vendors to allow the poten-tial customer to actually use theauthoring system in the demonstra-

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tion disc. Proprietary protectioncan be achieved by limiting thenumber of screens that can be builtor by not enabling a "save file"routine. It is virtually impossiblet get an indication of systemresponses, capabilities, perform-ance, and ease-or-use withoutactually using the authoring systemitself. This type of demonstrationdisc is widely rvailable in the"expert system shell" business, sovendors of expert systems can becontacted for details of logisticsand design. The expert systemshell demo discs do enable you tocreate and save your own mini-expert system knowledge base, andyet they are constraining enough sothat you could not actually producea fully operational expert systemwith them. Through the use ofthree of these discs we were able tomake a very intelligent evaluationof the different interfaces rrlativeto our needs.

2. Provide examples of different in-structional strategies in both thesales literature and on the demon-stration discs that can be achievedwith the authoring system. Definewhat you ::,ean by "tutorial" and"simulation", as these terms have avariety of meanings in the field.Show the types and varieties ofquestioning formats that areallowed within the constra:nts ofthe system.

3. Offer demonstration discs to poten-tial customers, if only on a "bor-rowed" basis. Potential customersshould not have to pay to obtainessential information to adequatelyevaluate a vendor's product.

4. Provide more technical informationon me performance of the author-ing system.

5. Include more information on thesystem's ability to interface wit'other software and programminglanguages. Also, discuss the capa-bilities to build library files ofgraphics and subroutines.

6. Include more i: .-ln.tation on theCMI package and its capabilitiesand applications. Give examplesof the tyres of records poss'ille and

identify how scorekeeping isaccomplished.

7. Discuss if and how feedback can becustomized to fit the needs of theuser. Include the constraints oncustomizing feedback as well ascapabilities.

REFERENCES1. Avner, A, S Smith, and P Tenczar.

CBI authoring tools: effects on pro-ductivity and quality. Journal ofComputer-Based Instruction 11:85-89, 1984.

2. Crider, J. Authoring syster. ;view:the educator. Journal of Computer-Based Instruction 11:101-102,1984.

3. Hannafin, MJ. Options for authoringinstructional interactive video.Journal of Computer-BasedInstruction 11:98-100, 1984.

4. Heck, WC and PE Grimm. Ease:easy authoring system for educators.Journal of Computer-BasedInstruction 11:72-73, 1984.

5. Hillelsohn, NJ. Benchmarkingauthoring systems. Journal ofComput' Sr-Based Instruction 11:95-97, 1984..

6. Kearsley, 0. Computer-BasedTraining. Addison-Wesley, NewYork, NY, 1984, pp 115-122.

7. Klass, R. The TenCore language andauthoring system for the IBMpersonal computer. Journal ofComputer-Based Instruction 11:70-71, 1984.

8. Locatis, CN and WI Carr. Using alesson-element keystroke orientedapproach for estimating authoringtool efficiency. Journal ofComputer-Based Instruction (in_ ss).

9. Locatis, C and V Carr. Authoringsystems and assumptions aboutthem. Journal of Bio Communica-tions 13:4-9, 1986.

10. Long, KT and 2C Squire. Course-ware's Apple authoring system.Journal of Computer-BasedInr-ution 11:74-75, 1984.

11. Reed, MJ and M Porter. A eviewof Digital's Courseware au'i.oringsystem. Journal of Comput er-Bas...dInstruction 11:68.69, 1984.

l2. Stein, I. The 1987 CBT Guide toComputer-Based Training Systemsand Coumware. Weingarten Publi-cations, Boston, MA, 1987,pp 1-37.

©1988 BY NATIONAL RESOURCE FOR COMPUTERS 111 LIFE SCIENCE EDUCATION


FROM CAI TO ICAI:TWO EXAMPLES ALONG THE WAYJoel A. Michael, Allen A Rovick, Martha Evens, Nahkoon Kim and Mohammed HagueDepartment of Physiology, Rush Medical College, Computer Science Department, Illinois Institute ofTechnriogy andDepartment of Information Science, Northeastern Illinois University, Chicago, Illinois

There is a continuum of computer-based exercises," each with its ownadvantages and limitations, that rangesfrom the "conventional" (CM) to the"intelligent" (ICAI). Application ofcign;fiennt cnrrepte frnm thn fielri ofartificial intelligence (AI) to the devel-opment of computer-based exercises,even in the absence of expensive AI"tools", can result iii innovative teach-ing programs.

We have already written several CAIexercises in physiology."'" For thepast two years, we have focused on de-veloping two tutoring programs in thedomain of the cardiovascular (CV)physiology. Neither program functionsat the level of an ICAI exercise; theyare not yet "smart tutors". But, wehave produced sophisticated programsthat are of use with our (JAM andAAR) students, and at the same time,are pointing the way to our eventualdevelopment of truly "smart" program.

WHAT IS A "SMART TUTOR"A "smart tutor" (sometimes referred toas an intelligent tutoring system or ITS)is a computer program able to assist astudent in mastering some subject mat-ter domain 4." The essence of such aprogram is that its tutoring its interaction with the student-- is context dependent.° This means that the part'cu-lar action it takes is determined by thecurrent state of the student's knowledgeor understanding. To produce such abehavior, a "smart tutor" must be ableto:

1) model the student's understandingof, or misconceptions about, thedomain being studied ;'

2) solve the problems being presentedto the student;

3) tutor the student, selecting a se-quence of actions (tactics) accord-

ing to some strategy that dependson the current state of the student;"and

4) communicate with the student in away that reveals what the studentunderstanzls; in =my, but not -all,cases this requirement can best bemet by the ability to generate atwo-way natural language conver-sation.°

As interim steps along the way to thedevelopment of our two "smart tutors",we have employed approaches to partially realize some of the above functions that do not require expensive orcomplex AI resources. Before describ-ing these solutions, however, we needto briefly describe the particular programs we are working on.

"SMART TUTORS" IN CVPHYSIOLOGYAs teachers of medical physiology, we(JAM and AAR) routinely ask studentsto learn the behavior of complex, nialti-component systems with feedback, andto do this in a way that facilitates theirability to "solve problems". Our programs are intended to assist students inthis task.

The first program assists students insolving pathophysiology problems.Here, the student is askol to first determine the pathophysiology giving rise toa constellation of signs and symptomspresented by a patient, and then to explain the origin of these features fromthe physiological cause they propose.5The general educational objective ofthis exercise is the development ofthose skills needed to apply a hypo-thetico-deductive problem-solvingapproach2 to a particular class ofproblems.

A second program coaches studentsworking with a computer simn,ation of

the system that maintains a constantblood pressure. Here, the studentpredicts the changes that will occur inseven parameters following selectedexperimental procedures (the tutor isbased on a leaching program calledCIRCSIM9. The general educationalobjective of this exercise is the devel-opment of the ability to analyze the behaav ic.-r. of complex systems possessingfeedh:Ick.6.5

INCORPORATING "INTELLIGENCE"INTO CAI EXERCISESWhile the present versiuns of these twoprograms are not as "smart" as theywill eventually be, they effectively per-form some of the functions necessaryfor an ITS without using sophisticatedAI techniques.

Student - computer interfaceEffective tutoring will require that thestudent be able to communicate withthe program using natural language,However, the development of a robustnatural language capability, one able tounderstand ill-formed inputs generatedby the student, and produce complex,context-dependent outputs, is a majorundertaking which we have onlystarted. In the absence of these capabil-ities we have developed two alternativeways for students to 21-ovide inputs thatcontain enough information to drive atutor;a1 interaction.

In the pathophysiology tutor' the stu-dent must:

1) identify patient signs andsymptoms,

2) generate hypotheses,3) request data,4) validate hypotheses, and5) provide explanations.

A menu-driven approach provides these

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functions at this time.identify pertinent ;atient findings,

the student uses a cursor to highlighttext in the description of the patient; theselected text is then saved to a file forlater use by the student and by the tutor.

To generate an hypothesis to accountfor the findings ae has selected, thestudent "pages" through a set of struc-tured menus until he finds an hypothe-sis that he identifies as the source of thepatient's problem. Data about the pa-tient is requested2.,v t`-e student in asimilar way. Wl. the set of possiblehypotheses or data is clearly finite, it issufficiently large that it places no sig-nificant constraint on the students'selection it does not suggest "an-swers" that the student might not havearrived at if he was working indepen-dently.

In our simulation-based program, thestudent makes predictions about the di-rection of change of specified param-eters using a predictions table in whichthe student can move a cursor .nd tog-gle the desired entry into each cell.This set of inputs pi-II/ides the tutorwith an assemblage of data from whichit can deduce a cognitive model of thestudent and then decide on the appro-prim response to generate. These isclew .y more information available herethan in a conventional CAI situationwhere a student answers a single ques-tion whose correctness is assessed andused to select feedback.

Expert problem - solversA "smart tutor" must itself "under-stand" the subject matter so that it cansolve the problems presented to the stu-dents and interpret students' responses.

For example, in a simulation-basedtutor, the simulation itself is an expertproblem-solver ;ti that it calculates thebehavior of the system for each input."Thus, whether the student is asked tomake qualitative pa.,lictions about thesystem, or to calculate the quantitativeresponses he expects, the correct an-swer is internally available.

Alternatively, in many domains it ispossible to develop rule-eased qualita-tive.models with which to generate pre-dictions and explanations about systemhehavior.'2 This is the approach that

we have taken with the preliminaryversion of our CV simulation tutor. Analgorithm has been mitten with whichthe qualitative behavior of the systemto any input can be determined. Suchan approach enables one to model com-plex systems where an analytical modelmay be too difficult to generate, orwhere sufficient data is not availableupon which to base such a model.

Where the nature of the exercise doesnot permit a model-based approach,one may use a stored data-base of solu-tions to the problems to be presented.This is the approach we have takenwith the pathophysiology tutor wherewe are dealing with a necessarily smallnumber of problems (both becausethere are a limited number of patientproblems that ace relevant to our educa-tional objectives, end because the timerequired to solve each problem is suffi-ciently long that the student can only doone or two problems at a sittine. Solu-tions to each of the three problems inour exercise are stored using an ap-proach that resembles a data-baseprogram.

Student-modellingConventional CAI ge.terally utilizesconditional branching from individualanswers 13 determine the particularfeedback to be provided or the next stepin the exercise to be pursued. Whilethis "individualizes" the students' ex-perience in sor limited sense, it pro-vides a fixed r; ponse to any wronganswer regardless of the reason thatthe student answered incorrectly.However, the same incoiTect responsecan result from great differences instrdents' general background, availableknowledge about the domain, or skill atproblem-solving

A "smart tutor" attempts to provide adeeper level of ass:stance to the studentby determining the specific basis forthe wrong answer. This process ofmodeling the "cognitive state" of thestudent is amolq;st the most difficultconfrontimg the development of ICAIprograms.

Our approach to student modeling inour simulation tutor, one with generalapplicability, is based on a concept mapof the rele rant domain and an experi-

mentally derived catalog of student"bugs". Predictions for a minimum ofseven parameters are available as eachexperiment is carried out. Particularpatterns of errors then allow the mod-eler to pick from the list of "bugs" themost probable source of the students'incorrect responses. For example, e,rors in predicting the changes that willoccur in CC, FM, and TPR and the fail-ure to have entered these predictionssequentially suggests that the studentdoes not understand the common neuralcontrol of Cie-se parameters. With suchinformation available to the "tutor", themost helpful intervention can then beselected.

SUMMARYThe development of sophisticated tu-toring programs, programs able to tailortheir interactions with the student tomeet the students' specific needs, willgo a long way towards overcomingmany of the limitations of current CAI.There is much to be learned from thefields of artificial intelligence and cog-nitive science that is relevant to this ef-fort, even if the technical resources toimplement these ideas are unavailable.It is ultimately the ideas that a mostrelevant, not the use of any particularAI programming language or AI workstation.

REFERENCES1. Clancey, WJ. Qualitative student

models. Ann Rev Comput Sri1:381-50, 1986.

2. Elstein, AS, LS Shulman, and SASprafka. Medcial problem solving:an analysis of clinical reasoning.Harvard University Press,Cambridge, MA, 197F

3. Hague, MM, M Even3, JA Michael,and AA Rovick. A physiology tutorbased on expert system. ProcESD/SMI 1st Ann Exp Sys Conf,Dearborn, MI, 1987, pp 423-436.

4. Lawler, RW and M Yazdani (eds).Artificial intelligence and education.Volume 1, Learning environmentsand tutoring systems. Ablex,Norwood, NJ, 1987.

5. Michael, JA and AA Rovick. CVpathophysiology problems in smallgroup tutorials. The Physiolot ist26:225-228, 1983.

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6. Michael, JA and AA Rovick.Computer simulated experiments inthe teaching of physiology. Proc 6thAnn Nat Educat Comput Conf,Dayton, OH, 1984, pp 20-24.

7. Michael, IA and AA Rovick. Computersimulations usein teaching: how togenerate them. Collegiate Microcom-puter 4:201-207, 1986.

8. Michael, SA ar' AA Rovick. Theuses of CBE in teaching the func-tions of complex systems. Proc 27thInternational ADCIS Conf, NewOrleans, LA, 1986, pp 43-48.

9. Rovick, AA and L Brenner.HEARTSIM: a cardiovascular simu-

AIMS AND SCOPE

lation with didactic feeuback. ThePhysiologist 26:236-239, 1983.

10. Rovick, AA and IA Michael.CIRCS1M: an IBM PC computerteaching exercise on blood pressureregulation. XXX Cong of Interna-tional Union of Physiol Sciences,Vancouver, BC, 1986.

11. Sleeman, D and JS Brown (eds).Intelligent tutoring systems.Academic Press, London, 1982.

12. White, BY and JR Frederiksen.Qualitative models and intelligentlearning envir vrients. In Lawler,RW and M Yazdani (eds), Artificialintelligence and education. Volume

Ir

1, Learning environments andtutoring systems. Ablex, Norwood,jiI, 1987 pp 281-305.

13. Woolf, BP. Context dependentplanning in a machine tutor. Unpub-lished doctoral dissertation, Depart-ment of Computer and InformationScience, University of Massachu-setts, Amherst, MA.

14. Yazdani, M. Intelligent tutoringsystems: an overview. In Lawler, RWand M Yazdani (ads), Artificial intel-ligence and education. Volume 'Learning environments and tutoringsystems. Ablex, Norwood, NJ, 1987,pp 183-203.

The goal of Compwcrs in Life Science Education is to provide a means of communicationamong life science educators who anticipate or are currently employing the computer as aneducational tool. The raage of content includes, but is rot limited to, articles focusing oncomputer applications and their underlying philosophy, reports on faculty/studentexperiences with computers in teaching environments, and software/hardware reviews inboth basic science and clinical education settings.

SUBSCRIPTION INFORMATION Computers in Life Science Education is published monthly by National Resource forComputers in Life Science Education, Mail Stop RC-7'), Univen.:ty of Washington, SeattiL,WA 98195. Subscription rate is $40.00 for 1i issues including postage and handling in theUnited States and Canada. Add 520.00 for postage (airmail) in Mexico and Europe andS23.00 for the rest of the world.

This newsletter has been registered with the Cop:ight Clearance Center, Inc. Consent isgiven for copying of articles for personal cy; internal use, or for the personal or internal use ofspecific clients. This consent is given on the condition that the ropier pay tnrough the Centerthe per-copy fee stated in the code on the first page for copying beyond that permitted by theUS Copyright Law. If no code appears on an article, the author has not given broad consentto copy and permission to copy must be obtained directly from the author. This consent doesnot extend to other kinds of copying, suchas for general distribution, resale, advertising andpromotional purposes or for creating new collective works.

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POSTMASTER: Send address changes to Computers in Life ,7citnee Education, NRC1.SE,Mail Stop RC-70, University of Washington, Seattle, WA 98195.


*IN1111.111.11.11.1mor .eak

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1/...1UME 5, NUMBER 12, DECEMBER 1988 CLSEE3 5(12), 89-96,1988 ISSN 0742-3233

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HAROLD I. MODELLDepartment of RadiologyUniversity of WashingtonSeattle. Washington


THEODORE J. CROVELLOGraduate Studies and ResearchCalifornia State University, Los AngelesLos Angeles. California


TSEGAYE HABTEMARIAMSchool of Veterinary MedicineTuskegee University.Tuskegee, Alabama



JAMES E. RANDALLDepartment of PhysiologyIndiana UniversityBloomington. Indiana





eOROTIIY WOOLEY-McKitYDepartment of BiologyGlendale Community CollegeGlendale, Arizona


/1IL

CONTENTS

uppprivo. ABREACT nv THE LITERATURE 89

INDEX FOR VOLUME 5 90

CUMULATIVE AUTHOR INDEX FOR VOLUMES 1-5 91

CUMULATIVE SUBJECT INDEX FOR VOLUMES 1-5 93

KEEPING ABREAST OF THELITERATURE

The following citations are presentedas part of a quarterly feature in CLSEdesigned to help readers become aware

f .urrent literature pertinent tocomputer applications in life scienceeducation.

Armistead LP et al: Impact of micro-computers on community collegefaculty. Community CollegeReview 15(2):38-44, 1987.

Bergeron BP: Toward more effectivelearning environments: Strategiesfor computer simulation design.Collegiate Microcomputer 6(4):289-309, 1988.

Browning ME et al: A problem solv-ing tutor for introductory genetics inTurbo PASCAL. CollegiateMicrocomputer 6(2):113-122, 1988.

Goolka;ian P et al: A computerizedlaboratory for general psycho:ogy.

Teaching of Pyschology 15(2):98-100, 1988.

Greaf WI): Computer-assisted instruc-tion: Is it right yell? Journal ofContinuing Eout..ation in the HealthProfessions 8(2):97-105, 1988.

Ingram D: Comp: ter models and sim-ulations in medical education.Interactive Learning Inv...mational4(2):3'.;-35, 1987.

MArcoulides GA: An intelligentcomputer based !caring program.Collegiate Microcomputer 6(2):123-126, 1988.

Micol JL: Two different approaches tocomputer-aided teaching ofmicrobial genetics. Comput ApplBiosci 3:89-92, 1987.

Pogue RE: Computers in nursingeducation. Whe-P. is the educationalperspective? Comput Nurs 6:94-96, 1988.

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90 COMPUTERS IN LIFE SCIENCE EDUCATION, VOLUME 5, NUMBER 12, DECEMBER 198R

Sarvela PD et al: Testing andcomputer -base' instruction: Psycho-metric considerations. EducationalTechnology 28(5):17-20, 1988.

Stonebraker PW et al: Teaching withpersonal computers. CollegeTeaching ,:69-74, 1988.

Strang HR: A microcomputer-basedlaboratory experience to assistundergraduate students in learning

basic concepts in human memoryand retention. CollegiateMicrocomputer 6(2):184-188, 1988.

Sturte. .:nt V et al: Micro-computertest - generation systems: A softwarereview. Teaching Sociology16(1):49-54, 1988.

Warner, M: Developing database filesfor student use. Computing Teacher15(7):44-47, 1988.

Watt ME et al: A tape-based system ofinteractive video for computerizedself-instruction. Med Teach 9:309-315, 1987.

Woods JW et al: Teaching pathologyin the 211' century. An experimentalautomated curriculum deliverysystem for basic pathology. ArchPathol Lab Med 112:852-856,1988.

COMPUTERS IN LIFE SCIENCE EDUCATION,VOLUME 5 INDEXanalog-to-digital cortv:Irsion, 4:26annual report, NRCLSE, 2:9-10Apple //e gameport, 4:26Apple //e workstations, 4:25-28authoring systems, 11:81-85

Barbee DD, 1:5-8Bl "kely L, 1:1-4bulletin board, 8:61-63

CLSE colleague directory, 2:11-15,3:17-23, 4:29-32

cardiovascular physiology, 1:5-6,7:49-56, 11:86

computer-uided instruction (CAI),11:86-88

Coon TG, 9:65-71

databases, laboratory, 1:3-4diagnosis game, 1:5-8diagnostic practice, 1:5-8diagnostic problems, 1:5-8

Erdman S, 10:75.-76evaluation

of authoring systems, 11:81-85of software, 10:73-75

Evens M, 11:86-88expert systems, 6:41-44EXSYS, 6:43-44

fault games, 1:5-8Feiner SA, 1:5-8

G.ahum MM, 8:57-60

Hague M, 11:86-88hemolysis, 4:26-27HyperCard, 5:33-34Hypertext, 5:33-34

intelligent computc...r-aided instraction(ICAI), 11:86-88

interface, photosensitive, 4:25-28

Keeping Abreast of dr Literature,3:23, 6:47-48, 10:76, 12:894/0

Kiel JW, 7:49-56N, 11:86-88

Kolitsky MA, 4:25-28

laboratory,data acquisition, 1:2-3physiology, 4:25-28photosensitive interface in, 4:25-28

laboratories, lessons from, 1:5-8language, graphic simulation, 7:49-56,

9:65-71literature, keeping abreast of, 3:23,

6:47-48, 10:76, 12:89

Macintosh, 5:33-35, 7:49-56, 9:65-71,10:7 -76

Michael JA, 11:86-88model, pulse pressure, 7:51-54Modell HI, 8:57-60, 10:73-75

Nagy MC, 6:41-44NRCLSE

annual report, 2:9-10software evaluation program,

10:73-75nuclear medicine, 8:57-60

peer critique of software, 10:73-75Peterson NS, 1:5-8photosynthesis, 1:3physiology,

cardiovascular, 7:50-56, 11:86muscle, 4:27plant, 1:1-4

respiratory, 1 :6-8, 4:27-28simulations, 7:49-56undergraduate, 4:25 28

plant physiology, 1:1-4pulse pressure, model, 7:51-54

red blood cell hemolysis, 4:26-27respiratory physiology, 1:6-8, 4:27-28Rqvick AA, 11:86-Er

Schultejann PA, 11:81-85Shepherd AP, 7:49-56simulation

laboratory, 8:57-60languag.:, . 19-56, 9:65-71wildlife managenh.t.t, n:66-70

smart tutors, 11:86-88software

authoring systems, 11:81-85available, 5:35-39, 6:45-46evaluation forms, 10:77-79evaluation program, 10:73-75expert systems, 6:43-44HyperCard, 5:33-34information report, 8:63LabView, 7:49-56peer critique of. 10:73-75

imulation, 7:49-56, 8:57-60,9:65-71

sources, 5:39, 6:46-47, 7:49, 7:56STELLA, 9:65-71

STELLA simulation :ftware, 9:65-71

Trainor MS, 11:81-85

veterinary curriculum, 10:75-76

well counter simulation, 8:57-60wildlife management, 9:66-70Wooley-McKay D, 5:33-34

i988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENCE EDUCATION 0742,3233/88/50.00+200

9,1

COMP N-L1FE SCIENCE EDUCATION, VOLUME 5, NUMBER 12, DECEMBER 1988 91

COMPUTERS IN LIFE SCIENCE EDUCATIONCUMULATIVE AUTHOR INDEX FOR VOLUMES 1-5Aucone, M. see Jones, 1.4.E.Aucone, Michael

see Jones, Michael E.Azbell, Janet W. Conversion of

instructional videotapes to vomputercontrolled formats for continuingmedical education. 3:27-30

Barbee, David D.see Peterson, Nils S.

Barr, Lloyd Computer-aided dataacquisition in an undergraduatephysiology laboratory. 1:19-22

Blakely, Larry First steps usingpersonal computers in a plantphysiology course. 5:1-4

Blanchaer, Marcel, C. PILOT: Thelanguage of choice for computer-assisted learning on microcomputers.3:37-39

Blanchaer, Marcel C. Stimulatingbasic health science learning withclinical simulations on amicrocomputer. 1:14-15

Blanchaer, M.C. Microcomputer-ledclinical case tutorials in basic science2,ducation. 3:25-27

Blumhardt, Ralphsee Lasher, John C.

Bolles, John R. Generic videodiscs:An educational resource. 2:73-76

Boyden, Patrick C. Which aspects ofsoftware are copyrightable? 2:57-57

Brubeck, J. Dale Networking CBE inhealth sciences. 1:41-44

Bushby, Philip A. Computerizedveterin"..4 medical informationsystems: Impact on veterinarymedical education. 3:33-36

Carden, Anthonysee Heckman, James

Carr, Victor see Locatis, CraigChurchill, Lynn D. Instructional

design issues and the effective use ofgraphics in computer-assistedinstruction. 2 :4 7

Clark, Carl 0. Computer interfacingfor science exp.,riments. 2:59-61

Coleman, Thomas G. Simulation ofbiological systems what should weexpect from such activity? 1:11-14

Collins, Michael A.J. The evolutionof a microcomputer center. 3:76-79

Comer, Ronald C. Professionals incomputer-based education ando-a;ning. 1:37-38

Coon, Thomas G. Using STELLAsimulation software in life scienceeducation. 5:65-71

Cooper, Elizabeth H. Simulatedexperiments in biology classes.3:57-59

Coyne, Mary D. Substitution of Applecomputers for storage oscilloscopesand polygraphs. 2:81-86

Craig, James F. From computerless tocomputer literate through resourcesharing. 4:89-91

Crovello, Theodore J. Computers inbiology education: Formalcoursework at Notre Dame. 1:44-46

Eckblad, James W. Experimentaldata simu:ations in biology. 4:60-63

Eckblad, James W. The transfer ofvideo images to microcomputers.2:78-79

Eckblad, James W. TransferringBASIC programs from the Apple H tothe IBM-PC. 3:49-52

Erdman, Susan A student's view ofcomputer-based curriculuminnovations. 5:75-76

Evens, Martha see Michael, Joel A.

Feiner, Stephen A.see Peterson, Nils S.

Fine, James S. Adaptation ofinteractive, videodisc in medicaleducation. 4:12-15

Ford, Richard T, Computer assistedinstruction for tekning dentaltreatment planning. 4:81-84

Giles, Roherl H. Uses of micro-computers in systems ecology andw :fe management instruction.4:9-12

Gordon, Albert see Kastella, KenGordon, Michael

see Heckman, JamesGraham, Michael M.

see Modell, Harold I.

Graham, Stephen N. Tools forcreating lessons on a computer. 2:1-5

Halsey, Eric Video signals anddevices in interactive video systems.3:12-15

Hague, Mohammedsee Michael, Joel A.

Harless, William G. Technology inmedical education a case studyapproach. 2:92-94

Harris, Michaelsee Heckman, James

Heard, Barbara K.see Heard, John, Jr.

Heard, John, Jr. Video-basedinstruction as an enhancement tocomputer learning. 2:43-45

Heckman, James The use ofcomputer animations during lecturesin physiology. 4:-3-76

Heckman, James see Wang, MichaelHeidcamp, William H. Computers

and undergraduate comparativephysiology. 1:54-55

Holliday, Charles W. Using"Human," a comprehensivephysiol3gical model, in teachik gundergraduate physiology courses.2:41-43

Jelemensky, Linda Craig Nursingeducation and microcomputers:Factors to consider. 2:67-71

Jensh, Ronald P. Use of computerassisted instruction interactive videoin basic medical education. 4:65-68

Johnson, Beth A. A survey ofmedical/health science interactiveinstructional materials. 2:49-56

Jones, M.E. Factors influencing thesuccess of CAI programs. 2:61-63

Jones, M.E. Failed CAI: Where didall the effort go? 2:65-67

Jones, M.E, Simulated laboratoryexperiments. 2:76-77

Jones, M.E. The use of drivers incomputer-assisted instruction.2:86-87

Jones, Michael E. Teaching clinicalmedicine using computer aidedinstruction. 3:59-62

07423233/8840.00 + 2.00 1988 BY NATIONAL RESOURCE FO,. COMPUTERS IN LIFE SCIENCE EDUCATION

92

92 COMPUTERS IN LIFE SCIENCE EDUCATION, VOLUME 5, NUMBER 12, DECEMBER 1988

Kabo, J. Michael Computerreconstruction of a human arm fromserial sections. 2:14-16

Kastella, Ken Use of electronicblackboard teleconference teaching ata remote site. 4:33-35

Kiel, J.W. A graphic computerlanguare for physiology simulations.5:49-56

Kim, Nahkoon see Michael, Joel A.Knoll, K. Richard

see Stull, Richard E.Knoll, Richard sce Stull, RichardKolitsky, Michael A. Apple He

mediated workstations in theundergraduate physiology laboratory.5:25-28

Lancaster, Jack L.see Lasher, John C.

Larson, Donna E. Using computes '-assisted instruction in the education ofhealth care professionals: What thedean need:, to know. 1:65-67

Lasher, John C. Teaching radiologicprinciples by microcomputer in smallgroup conferences 1:29-32

Levine, Sherman D. Microcomputergraphics as a substitute for lectures'des 1:33-36

Locatis, Craig Factors to considerwhen choosing an authoring system.2:5-7

Macias, John D. MENTOR: Acomputer program to :each thedifferential diagnosis process.3:52-55

Maffly, Roy H. see Macias, John D.Malone, Judy A. Computer culture

shock: A case for affective education.2:9-11

Mann, J.W. see Jones, M.E.Mann, John W. see Jones, Michael E.Manski, Richard sec Craig, James F.Meals, Roy A. see Kabo, J. MichaelMeinke, W,J. Computer-based exam

construction in microbiology andimmunology: the MITI bank. 1:39

Meyers, Roy S. Creative educationaluse of software written by others.4:25-32

Michael, Joel A. Computersimulations in life science education:Some design issues. 3:73-76

Michael, Joel A. From CAI to ICAI:Two examples along the way,5:86-88

Michael, Joel A. Letter to the editor:Comment on peer review or peercritique of software. 4:91-92

Michael, Joel A. Making CBEprograms "smart": One goal ofartificial intelligence research.3:19-22

Michael, Joel A. see Rovick, Allen A.Mikiten, Terry M. Enhancing lec-

tures with a microcomputer. 1:9-11Modell, Harold I. Approaches to

simulations for teaching. 3:41.45Modell, Harold I. Editorial Comment:

The role of computers in the studentlaboratory. 4:25-30

Modell, Harold I. Editorial Comment:Reflections and future directions.2:89-92

Modell, Harold 1. Establishment ofthe National Resource for Computersin Life Science Education. 3:1-5

Modell, Harold I. How shall weevaluate software peer review orpeer critique? 4:68-69

Modell, Harold I. Input/output designfor different educational settings.1:57-62

Modell, Harold I. NRCLSE beginssoftware evaluation program. 5:73-75

Modell, Harold I. Use of a computersimulation to reinforce wet labs innuclear medicine. 5:57-60

Modell, Harold I. Using the computerin the classroom first steps. 1:1-3

Modell, Harold I. Technical aspectsof using microcomputers in a groupsetting. 2:35-38

Modell, Harold I.see Michael, Joel A.

Mosley, Mary Lou Solving aninstructional problem with inter.,divevideo. 1:49-50

Moore, John F. An entry-levelapproach to interactive video programdevelopment. 3:r 12

Murphy, Bruce J. Developingcomputer assisted instruction foranatomical science education.3:65-67

Nagy, Monica C. An overview ofexpert systems. 5:41-44

Olivo, Richard F. Selecting ananalog-digital interface: A tutorial.3:89-93

Parsons, Robert see Meyers, Foy S.

Peterson, Nils S. Diagnostic practicereinforces lessons from laboratories.5:5-8

Robinson, Steven L. DISCOURSEA technology based alternative forgroup instruction 4:17-20

Rothe, Carl F. Cardiovascularinteractions a simulaf:on package tohelp learning. 4:49-53

Rovick, Allen A. The computerlesson. 1:6-7

Rovick, Allen A.see Michael, Joel A.

Schotteliui,, Byron A. Teachingphysiology by microcomputer insmall group conferences. 1:4-6

Schuitejann, P. Alexandrasee Trainor, Mary S.

Shepherd, A.P. see Kiel, J.W.Skiba, Diane J. Evaluation of

computer-assisted instructionc''urseware. 2:11-14

Sprague, Ruth M. JANUS Acomputer interface betweenlaboratory and lecture. 3:69-70

Stein, Randall see Barr, LloydStevens, F.C. see Blanchaer, M.C.Story, Naomi 0.

see Mosley, Mary LouStull, Richard Creative equipment

acquisition for development ofcomputer software. 2:33-35

Stull, Richard E. Projecting computerdisplays. 4:76-77

Townsend, DeWaynesee Ford, Richard T.

Trainor, Mary S. Selecting anauthoring system 1988: How thevendors make it mere difficult.5:81-85

Troncale, Joseph A. Computer-assisted instruction in a familypractice curriculum: Two years later.3:17-19

Tucker, Clyde How can you deter-mine which educational software isbest for your application? 1:51-53

Tymchyshyn, Patricia Computerproliferation: An experience to share.1:67-70

Vincenzi, Frank F. Projectingmicrocomputer images in theclassroc a comment en twoprojection monitors. 1:17-19

©1988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENCE EDUCATION 0742-3233/8840.00 + 2.00

COMPUTERS IN LIFE SCIENCE EDUCATION, VOLUME 5, NUMBER 12, DECEMBER 1988 93

Walters, Jimsee Mose ly, Mary Lou

Wang, Michael Computer-basedlaboratory exercises. 4:57-60

Wang, Michael B.see Heckman, James

Washington, Dany J. Designingcomputer assisted instructionmaterials for remedial freshmanbiology curriculum 2:17-22

westmoreland, Nelson A graphics

oriented database for anatomy andphysiology. 4:54-56

Woods, James W. Letter to the editor:Comment on PILOT. 3:55

Woods, James W. Optical videodiscsand computers in life scienceeducation. 1:25-28

Woo ley-McKay, Dorothy HyperCard- What is it? 5:33-34

Wooley-McKay, Dorothy Planningand developing a microcomputer

laboratory for use in a communitycollege biology curriculum. 3:67-68

Wooley-McKay, Dorothy Use ofcomputer tutorials in communitycollege human anatomy andphysiology classes. 1:36-37

Yoder, Marianne E. Syntf r differ-ences between SuperPILO" andPC PILOT. 4:20-22

COMPUTERS IN LIFE SCIENCE EDUCATIONCUMULATIVE SUBJECT INDEX FOR VOLUMES 1-5Affective education 2:9-11Analog-to-digital conversion

3:89-93, 4:28-30, 5:26Anatomy 1:36, 3:65-67, 4:54-56,

4:65-68of arm 2:14-16

Animal behav:9r data simulation4:61

Animation 2:47, 4:73-76Annual repo' I, NRCLSE 5:9-10Apple PILOT 3:38Apple H, IBM-PC programconversion 3:49-52Apple He gameport 5:26Apple He workstations 5:25-28Appleroft BASIC 3:50-51Arm, anatomy of 2:14-16Artificial intelligence 3:19-22

information sources 3:22Aaultation 1:49Authoring systems 5:81-8cAxon

action potential model 2:26-29simulation of 1:5

BASIC, transferring programs 3:49-52BASICA 3:50-51Basic science

computer simulations and 1:14Biological systems, simulation of

1:11-14Biology 1:44, 4:60-63

microcomputer laboratory for3:67-68

remedial education in 2:17-22simulated experiments in 3:57-59workstations in 2:81-82

Bulletin board 1:70-71, 2:39, 3:6,3:30-31, 3:79, 4:78, 5:61-63

Carbon dioxide transport 1:58Cardiovascular simulations 1:5,

4:49-53Cardiovascular physiology 4:28,

4:49-53, 4:54-55, 4:73-76, 5:5-6,5:49-55, 5:86

Case studies 2:92-94Classroom use of computers 1:1-3Clinical case tutorials 4:25-27Clinical medicine. using computer

aided instructic .or 3:59-62Clinical simulations 1:14CLSE colleague directory 3:81-87,

4:1-8, 4:15, 5:1 i-15, 5:17-23, 5:29-32Color 2:47Communication system 4:17-20Computer-assisted instruction

5:86-88for anatomical science education

3:65-67in clinical medicine teaching 3:59-62for dental treatment p' ming 4:81-84drivers in 2:86-87in family practice curriculum 3:17-19graphics in 2:45-47in nursing 2:67-71in remedial biology 2:17-22success of 2:61- 63,2:65 -67videodiscs in 2:4:,45, 2:73-76,

2:92-94, 4:65.68Computer-assisted learninglanguages 5:36-39

Computer lesson, structure of 1:6Computer literacy 4:89-91

effective teaching of 2:9-11COMPUTERSCOPE 2:81-86Conferpnce0eaching in, 1:4, 1:60Copyright 2:57-59.,Cost-effecti4ness of CAI 1:66

Coursewareavailable 2:23-24, 2:49-56copyright of 2:57-59creation of 2:evaluation of 2:11-14remedial 2:17-22

Data acquisition 1:19, 1:54Data simulations 4:60-63Databasegraphics oriented 4:54-56laboratory 5:3-4

Database services 1:42Debuggy, smart tutor program 3:20-21Dental education 4:81-84; 4:89-91Dental treatment planning 4:81-84Diagnosis

game 5:5-8simulated 1:15

Diagnostic practice 5:5-8Diagnostic problems 5:5-8Differential diagnosis process,

computer program for teaching3:52-55

Dig;tal image processors 2:77-79Dig tal-to-analog conversion 3:91Direct memory access 3:92Discourse 4:17-20Drills 1:54Drivers 2:86-87

Ecology 4:9-12, 4:61Education

anatomical science 3:65-67basic science 3:25-27continuing medical 3:27-30grade analysis software 3:63medical 4:12-15, 4:65-68test generator software 3:63

0742-3233/88/SO.00 + 2.00 1988 BY NATIONAL RESOURCE FOR COMPUTERS IN LIFE SCIENCE EDUCATION


veterinary medical 3:33-36Electrohome projection monitor

1:18Electronic blackboard 4:33-35Evaluationof authoring systems 5:81-85of software 5:73-75

Examinations 1:39drivers and 2:86-87in remedial biology 2:20-21

Experimentsinterfacing with 2:59-61simulations of 2:76-77

Expert systems 5:41-44EXSYS 5:43-44

Family practice 3:17-19Fault games 5:5-8Filevision 4:54-56Function generators, use in

simulations' 3:44-45

Genetics, simulation 3:58-59Graphicsdissociation curves and 1:58from HUMAN model 2:30-32in radiology 1:30oriented database 4:54-56slides and 1:33

Group instruction 1:4-6, 1:9-11,1:17-19, 1:29-32, 1:33-36, 1:57-62,2:35-38, 2:92-94, 4:17-20, 4:33-35,4:73-76

Hardwareaccess to 2:33-35for aro anatomy 2:14-16autho..ag systems and 2:6for interfacing 2:61for oscilloscope substitution 2:81-82for physiology lab 1:20

Health care 1:65Health Education Netw- lc 2:89Hematology, videodisc.. 4:12-15Hemolysis 5:26-27Hodgkin-Huxley

model 2:26-29squid axon simulation

HUMAN model 2:29-32, 2:41-43HyperCard 5:33-34Hypertext 5:33-34

IBM-PC, Apple II programconversion '3:49-52

Immunology 1:39Input-output design 1:57Information systems 3:33-36

Intelligent computer-aidedinstruction (ICA') 5:86-88

Interactive videoapproach to program development

3:9-12conversion of videotape 3:27-30in hematology 4:12-15in medical education 4:65-68signal processing 3:12-15

Interactive video system 1:49Interface, photosensitive 5:25-28Interfacing 2:59-61

laboratory/lecture 3:67-68

JANUS, interface between laboratoryand lecture 3:67-68

Keeping Abreast of the Literature1:23-24,2:71-79,3:70-71,4:93-94,

1:47, 1:69-70, 2:22-23, 2:48,9:94-95, 3;22-2'1, 3:46-47,3:93-94, 4:23, 4:47, 4:69-71,5:23, 5:47-48, 5:76, 5:89-90

Laboratories, lessons from 5:5-8Laboratorydata acquisition in 1:19, 5:2-3exercises 4:25-30, 4:49-53, 4:57-60,

4:60-63microcomputer 3:67-68ph -lology 5:25-28photosensitive interface in 5:25-28simulated microbiology 3:57-59

Laboratory/lecture interface 3:67-68Languages 4:14, 4:20-22

authoring 2:3-4graphic simulation, 7 49-56, 9:65-71

Learning center 4:89-91Lectures, computer use in 1:9, 1:59,4:7348

Liquid crystal display projectors4:76-77

Lung sounds 1:49

Macintosh 4:54-56, 5:33-35, 5:49-56,5:65-71, 5:75-76

MatALOT 3:38-39Magnetic resonance imaging 1:29Mastery oriented teaching unit

3:66-67Mathematical models 1:13MENTOR, program to teach

differential diagnosis process 3:52-55Microbiology, simulated laboratory in

3:57-59, 4:28Microbiology-Immunology Test Itembank 1:39

Microcomputer center 3:76-79

Microcomputer h.boratory 3:67-68Microcomputers

interfacing to 2:59-61in nursing 2:67-71simulations on 2:35-38video images on 2:77-79

Models, see also simulationsHUMAN 2:29-32, 2:41-43nerve 2:26-29, 2:82-85pulse pressure 5:51-54structure of 1:13TIME 2:92-94

Multiple choice questions 1:15, 2:62,2:86-87

Multiuser system 1:20Muscle physiology 4:57-60, 4:73-76,

4:85

National Resource for Computers inLife Science Education 2:91, 3:1-5annual report 5:9-10software evaluation program 5:73-75questionnaire 3:7, 4:87

Nerve conduction velocity 2:82-85Networking, national 1:41Neuroscience Software Project 1:9NRCLSE see National Resource for

Computers in Life Science EducationNuclear medicine 4:26-27, 5:57-60Nursing 1:67, 2:67-71

skills simulation A:26-27

Optical videodiscs 1:25Oscilloscopes 2:81-82Osmotic pressure 1:34Oxygen transport 1:58

Pathology 4:12-15, 4:65-68Patient simulation 2:92-94PC/PILOT 3:38Peer critique 4:68-69, 4:91-92,

5:73-75Peer review 4:68-69, 4:91-92Photosynthesis 5:3Physiology 1:4, 1:36, 4:27-30,

4:54-56, 4:61, 4:73-76cardiovascular 4:49-53, 5:5-6,

5:50-56, 5:86comparative 1:54HUMAN model of 2:29-32, 2:41-43Laboratory data acquistion and 1:19muscle 4:57-60, 5:2'/plant 5:1-4respiratory 4:28, 5:6-8, 5:27-28simulations 5:49-56undergraduate 5:25-28

Pictures, storage of 1:26

©1988 BY NATIONAL RESOURCE FOR COMPUTERS IN MT SCIENCE EDUCATION 0742-3233/88/S0.00 + 2.00

COMPUTERS IN LIFE SCIENCE EDUCATION, VOLUME 5, NUMBER 12, DECEMBER 1988 95

PILOT 3:36-39, 3:55, 4:20-22sources of information 3:39

Plant physiology 5:1-4Polygraphs 2:81-86Problem sets 1:2PRODUCER 4:14Programmed learning material 1:2Programs, see also software

guidelines for 1:22preparation time for 1:35

Projection monitors 1:17, 4:76-77Pulse pressure, model 5:51-54

Radiology 1:29Red blood cell hemolysis 5:26-27Remedial education, in biology

2:17-22Renal glomerular circulation 1:34Respiratory physiology 4:28, 5:6-8,

5:27-28

Simulation 1:2, 2:62, 4:25-30approaches for teaching 3:41-45approaches in designing 3:74as a substitute for labs 3:59biological systems 1:11-14biology experiments 3:57-59,

4:60-63cardiovascular system 3:62-63,

4:49-53clinical 1:14design issues 3:73-76of experiments 2:76-77, 4:57-60,

4:60-63laboratory 5:57-60language 5:49-56, 5:65-71as lecture aids 2:35-38

in microbiology 3:57-59of muscle 4:57-60nursing skills 4:26-27patient 2:92-94, 3:63physiology 3:62-63respiratory system 3:4, 4:28wildlife management 5:66-70

Slides, graphics in lieu of 1:33Smart tutors 3:19-22, 5:86-88Software 1:62

authoring systems 5:81- °5available 2:23-24, 2:4: j6, 4:36-39,

4:41-45, 5:35-39, 5:45-46copyright of ::57-59creation of 2:1-7evaluation forms 5:77-79evaluation of 1:51, 2:11-14, 2:70-71,

4:68-69, 4:91-92evaluation program 5:73-75expert systems 5:43-44graphics 1:30HyperCard 5:33-34interfacing and 2:60-61LabView 5:49-56peer critique of 5:73-75for physiology lab 1:21simulation 5:49-56, 5:57-60, 5:65-71sources of 2:23-24, 3:62-63, 4:36-39,4:41-45, 5:39, 5:464, 5:49, 5:56

STELLA 5:65-71Sony projection monitor 1:18Sophie, smart tutor 3:21Sound in instruction 1:50STELLA simulation software

5:65-71Student laboratories 4:25-30,

4:49-53, 4:57-60, 4:60-63

Students 2:66-67affective teaching of 2:9-11interactions with 2:13view of computer-based curriculum

5:75-76SuperPiLOT 3:38, 4:14, 4:20-22Systems ecology 4:9-12

Tabular data 1:60Teaching at a remote site 4:33-35Telecommunications ::42Teleconferencing 4:33-35TIME project 2:92.9ATutorials, in basic science education

3:25-27

Veterinary curriculum 5:75-76Video signals and devic/ts 3:12-15composite signals 3:14-15RGB signals 3:15

\:ideo worbstatN. 4:65-68Videocam with interactive

videotape 3:29Viceodisc 1:25, 2:43-45, 2:92

dew. )pment 4:13-14generic 413-76hardware selection 4:14, 4:65-68language selection 4:14

Videotapeconversion 3:27-30use with interactive video 3:5 -12

Voltage clamping 1:5

Well counter simulation 5:57-60Wildlife management 4:20-22,

5:66-70

A limited number of volumes 1,2,3, and 4 of CLSE areavailable for purchase. The cost per volume is $30.00 (US)plus shipping. Address orders to:

NRCLSE, Mail Stop RC-70University of Washington

Seattle, WA 98195

0742-3233/88/S0.00 + 2.00 ©1988 BY NATI DNAL RESOURCE FOR COMPUTERS IN I," S SCIENCE EDUCATION

6


AIMS AND SCOPE The goal of Computers in Life Science Education is to provide a means of communicationamong life science educators who anticipate or are currently employing the computer as aneducational tool. The range of content includes, but is not limited to, articles focusing oncomputer applications and their underlying philosophy, reports on faculty/studentexperiences with computers in teaching environments, and software/hardware reviews in




Articles submitted for publication should not exceed 2000 words and should be typewritten,double spaced, with wide margins. The original and tw, copies including two sets of figuresand tables should be sent to the Editor: Dr. Harold Modell, NRCLSE, Mail Stop RC-70,University of Washington, Seattle, WA 98195.



Reference style and form should follow the "number system with references alphabetized"described in the Co mil of Biology Editors Style Manual. References should be listed inalphabetical order by the first author's last name, numbered consecutively, and cited in thetext by these numbers.



SUBSCRIPTION INFORMATION Computers in Life Science Education is published monthly by National Resource forComputers in Life Science Education, Mail Stop RC-70, University of Washington, Seattle,WA 98195. Subscription rate is 540.00 for 12 issues, including postage and handling in theUnited States and Canada. Add $20.00 for postage (airmail) in Mexico and Europe and523.00 for the rest of the world.





©1988 BY NATIONAL RMURCE FOR COMPUTERS IN LIFE SCIENCE EDUCATION 0742.3233/88/S0.00 + 2.00

Documents

DOCUMENT RESUME ED 302 420 TITLE 5, 1986. 88 97p. 1988 · 2014. 3. 24. · DOCUMENT RESUME ED 302 420 SE 050 258 TITLE Computers in Life Science Education. Volume 5, 1986. INSTITUTION