Colaborative Learning in Education

Collaborative learning with screen-basedsimulation in health care education: an empiricalstudy of collaborative patterns andproficiency developmentjcal_407 448..461

L.O. Häll,* T. Söderström,* J. Ahlqvist† & T. Nilsson†*Department of Education Umeå University, S-901 87 Umeå, Sweden†Oral and Maxillofacial Radiology, Department of Odontology, Umeå University, S-901 85 Umeå, Sweden

Abstract This article is about collaborative learning with educational computer-assisted simulation(ECAS) in health care education. Previous research on training with a radiological virtualreality simulator has indicated positive effects on learning when compared to a more conven-tional alternative. Drawing upon the field of Computer-Supported Collaborative Learning, weinvestigate collaborative patterns, their causes, and their implications for learning. We investi-gate why the extent of application of subject-specific terminology differs between simulationtraining and more conventional training. We also investigate how the student-simulator interac-tion affordances produce collaborative patterns and impact learning. Proficiency tests beforeand after training, observations during training, and interviews after training constitute theempirical foundation. Thirty-six dentistry students volunteered for participation. The resultsshowed that not only the task but also the medium of feedback impacts the application ofsubject-specific terminology. However, no relation to proficiency development was revealed.We identified turn-taking as well as dominance patterns of student-simulator interaction butagain found no relation to proficiency development. Further research may give us deeperinsights into if and how these collaborative patterns, in other respects, impact collaborativelearning with ECAS in health care education.

Keywords collaboration, computer, education, health care, learning, simulation.

Introduction

Applications of educational computer-assisted simula-tions (henceforth called ECAS) are spreading withinhealth care education (Issenberg et al. 2005; Nehring &Lashley 2009). This has been described as in part aneffect of a paradigm shift of educational models, froman apprenticeship model for learning based on experi-ence to a model based on expertise (Luengo et al. 2009),

although other practical factors are likely to play signifi-cant roles (Gaba 2004). A primary function for theECAS is to ensure patients’ safety through ensuringpractitioners’ competence when training on actualpatients, and is limited or non-existent (ibid.). Previousstudies have demonstrated both positive as well as nega-tive effects on skill or proficiency development, often incomparison to available, more traditional alternatives(e.g. Engum et al. 2003; Quinn et al. 2003; Nilssonet al. 2007; Söderström et al. 2008).

New technologies have a tendency to attract the atten-tion of educators. Some of which read much hope into

Accepted: 28 September 2010Correspondence: Lars O. Häll, Department of Education, Umeå Uni-versity, S-901 87 Umeå, Sweden. Email: [email protected]

doi: 10.1111/j.1365-2729.2011.00407.x

Original article

448 © 2011 Blackwell Publishing Ltd Journal of Computer Assisted Learning (2011), 27, 448–461

them for the realization of their educational ideals,while others view them as threats to the current educa-tional canon (Cuban 2001; Dillenbourg et al. 2009;Säljö 2010). The tension between enthusiasts and criticscan create an initial dichotomy of general statementsabout the technologies’ effect on education and blackbox comparisons of ‘new’ versus ‘old’ media. This ten-dency can be traced in the earlier research of health caresimulation as well (e.g. Abrahamson et al. 1969; Ovasa-pian et al. 1988; Chopra et al. 1994; Peugnet et al.1998). However, the tension between enthusiasts andcritics can also drive the research to be more nuancedand practice-oriented studies where the technique ofusing the technologies enter focus. Later research onhealth care simulations seem to have shifted to thisfocus. (Gaba 2004; Issenberg et al. 2005; McGaghieet al. 2010). It is to this research that we want to contrib-ute by adding collaborative learning to the equation.

From a Computer-Supported Collaborative Learn-ing’s (CSCL) perspective, collaborative activities maybe put forward as a framework technique to improvetraining efficiency with this type of technology (e.g.Crook 1994; O’Malley 1995; Koschmann 1996; Dillen-bourg et al. 2009). Collaboration is, however, not con-sidered to support learning automatically, and a generalCSCL question for Dillenbourg is thus ‘under whichconditions is collaborative learning effective?’ with thesub-questions ‘under what conditions do specific inter-action patterns occur’, and ‘what interaction patternspredict learning outcomes?’ (ibid. p. 6). These generalquestions are valid also for this study of learning with anECAS in dentistry education.

Within the research project ‘Learning Radiology inSimulated Environments’, we have studied collabora-tive learning with one type of ECAS, a screen-basedvirtual reality (VR) simulator1 (Söderström et al. 2008).In a prior paper, we presented comparative data on pro-ficiency development and collaborative patterns for stu-dents’ training with the simulator (the SIM group), ascontrasted to students working with a more conven-tional PowerPoint-based exercise (the CON group). Inshort, we found that while collaboration in the CONgroups seemed to be more in line with our ideals, e.g.with more inclusive peer discussions, more thoroughinterpretations of what is shown on screen and moreextensive application of subject-specific terminology, itwas the SIM groups that developed more with regard toproficiency development (ibid.). Others have argued

that students may work with software without muchreflection or change in their conceptual understanding,and argued for the benefits of thorough dialogue (e.g.Pilkington & Parker-Jones 1996; Pilkington 1998). Thisdiscrepancy thus intrigued us and is part of our motiva-tion to keep analyzing this data.

In this article, we will continue the analysis of col-laborative learning with the radiological VR simulator.It is written in the intersection between the empiricalresearch field of learning with ECAS within health careeducation and the general research field of CSCL.Based on Dillenbourg’s general questions, two themeswill be investigated, described in the next section alongwith the empirical questions about observed collabora-tive patterns, their causes, and their impact on learningfor dentistry students’ training collaboratively with theradiological VR simulator.

The first theme is about a specific collaborativepattern, the application of subject-specific terminologyduring the training session. In sociocultural perspec-tives on learning, action with cultural tools or medita-tional means is one central concept (e.g. Wertsch 1998;Säljö 2010). These tools can be physical artefacts suchas an X-ray machine, as well as mental systems such asparallax technique for interpreting X-ray photographs.Some argue that a key aspect of adult learning, such asprofessional learning of the practice of radiologicalexaminations, lies in the mastery of the cultural toolsrequired for competent action within this practice. Thismastery entails not only developing competence inapplying specific tools but also being able to chooseappropriate tools and design solutions to specific tasks(Säljö 1999, 2000, 2010). Verbal interaction is a recur-ring focus of sociocultural studies because it can revealparticipants’ use of mental tools and collaborative foci(e.g. Enyedy 2003; Mercer 2005), as well as non-verbalactions and interactions have proven to be valuable ele-ments of observation when researching health care edu-cation (e.g. Hindmarsh 2010). In this article, theapplication of subject-specific terminology is regardedas a visible indicator of students trying to use culturaltools from specific subject areas. The extent to whichgroups in SIM and CON apply subject-specific termi-nology during training was analysed, and it was foundthat SIM groups tend to apply them to a significantlylesser degree (Söderström et al. 2008). The empiricalquestions for this theme are (1) [W]hat causes the extentof application of subject-specific terminology to differ

Collaboration and educational simulations 449

© 2011 Blackwell Publishing Ltd

between groups working with the ECAS (SIM) andgroups working conventionally (CON)? and (2) [W]hat,if any, implications does the extent of applicationof subject-specific terminology have for proficiencydevelopment?

The second theme is about one of the fundaments ofECAS, the physical interaction with the simulator itself,and how this impacts collaboration and learning. Thephysical student–simulator interaction is essential forenabling students to inquire or discover (e.g. Lazonderet al. 2010) that which is simulated, or to learn toperform it (e.g. Engum et al. 2003). With a screen-basedsimulator such as the one under scrutiny here, manoeu-vring the simulator became a central activity. While allparticipants are able to see the screen at any givenmoment, they cannot all manoeuvre it at the same time.This separates it from some full body computer assistedmannequins, or ‘integrated simulators’ (Bradley 2006),where participants interact with different parts of thesimulator. Manoeuvring the simulator means control-ling what is shown on screen and has thus a directimpact on what visual information is available to thegroup. It is thus justified to investigate how the physicalstudent-simulator interaction affordances impact col-laboration and learning. The empirical questions for thistheme are (3) [A]re there distinguishable patterns ofhow students share control over the simulator?; (4)[D]o the patterns of control over the simulator on thegroup level, or time spent manoeuvring the simulator onthe individual level directly impact proficiency develop-ment?; (5) [D]o specific patterns of control over thesimulator produce specific patterns of verbal interac-tion and does individual control over the simulator alsoentail control over the verbal interaction?; (6) [A]re theverbal patterns or individual control over the verbalinteraction directly related to proficiency develop-ment?; and (7) [H]ow is the group collaboration aroundthe student–simulator interaction perceived by the stu-dents themselves?

This article contributes to the growing interest oftechniques for using ECAS in health care education.Drawing upon the field of CSCL we will contribute tothe investigation of collaborative learning as such, atechnique with focus on relationships between culturaltools, interactive patterns, and learning. How does thetransition from one tool (MS PowerPoint) to another(screen-based VR simulation) impact the application ofother tools (subject-specific terminology), peer collabo-

ration and learning outcomes? Can we discern charac-teristics of the tools central to these effects? What arethe implications for designing for collaborative learningwith screen-based simulations in health care educationand for future research on the subject?

Method

This study was built around a quasi-experimental core.Participants were recruited from a population of under-graduate students in the dentistry program at UmeåUniversity, Sweden, attending a course in Oral andMaxillofacial Radiology. Thirty-six students – 20female and 16 male – volunteered. The core of thedesign was that (1) [I]nitially, all participants performeda proficiency test. (2) [A]n experimental group (SIM)and a control group (CON) were formed, with 18 stu-dents in each group, matched to be comparable on theproficiency test. Within the respective group, six workgroups of three students were randomly created, result-ing in six SIM groups and six CON groups. (3) [T]heworkgroups received training on the subject of objectlocalization using parallax principles, i.e. analysingmultiple X-ray photographs of a jaw in order to identifyand position potential anomalies in relation to genericanatomical objects such as specific teeth. During thistraining SIM used a screen-based simulator and CONused a set of MS PowerPoint slides. These sessions wererecorded on video. (4) [A]ll students performed asecond proficiency test after training. Later on 18participants, nine from each group, participated infollow-up interviews. This design is illustrated inTable 1. Each of these elements is explained furtherin the next section.

The proficiency tests

The students’ proficiency in interpreting radiographswas evaluated before and after the exercises. The instru-ment used, a proficiency test, was developed by twodental scientists who teach at the dentistry program. Itmeasured what right now counts as subject proficiencyin this course at the dentistry program.

The tests consisted of three subtests: a principle test, aprojection test, and a radiography test; each part gradedfrom 0–8 giving a total of 24 points. The principlesubtest aimed at assessing the participants’ understand-ing of the principles of motion parallax. The projection

450 L.O. Häll et al.


subtest aimed at assessing the participants’ ability toapply the principles of motion parallax. Based on basicsketches, this subtest requires basic understanding ofanatomy. The radiography subtest aimed at assessingthe participants’ ability to locate object details inauthentic radiographic images utilizing motion paral-lax. The participants were asked to report the relativedepth of specified object details in pairs of radiographs.The proficiency analyses in this study are based on thetotal score from all three tests.

Training sessions and simulator

The training session settings were comparable for theSIM and CON groups, with the exception of softwareand computer peripherals. The training tasks focused onobject localization using the principles of parallax. Thework groups (with three students in each group) werelocated in front of a PC equipped with either the radio-logical VR simulation software, illustrated in Fig 1, or aset of PowerPoint slides with images and written tasksand subsequent answers, illustrated in Fig 2. Please note

that while Fig 1 illustrates single-user simulation train-ing, the training discussed in this article was performedin workgroups with three students in front of thescreens. A passive teacher was available during the60-min-long sessions.

The simulator was basically a PC equipped withsimulation software, two monitors, and some specialperipherals. Special glasses were worn by all groupmembers to enhance the simulator’s 3D effects. One ofthe monitors represented 3D models of a patient,camera, and film. The other represented virtual X-rayphotographs. The control peripherals used for interac-tion include a standard keyboard, a 3D mouse, and apen-like controlling device. While the keyboard is usedscarcely, the pen and the mouse are central to manoeu-vring the simulator.

Using the simulator, the students were able to col-laboratively perform real-time radiographic examina-tions of patients’ teeth. It allowed the students toposition the three-dimensional model of the patient, theX-ray tube, and the film. X-ray images could then be

Table 1. Design of the study.Input Process Output

Variable Pre-trainingproficiency

Training SIM Post-training proficiencyTraining CON Experience

Evaluationof variable

Proficiency test Observation Proficiency testSurveyInterviews

SIM, simulator; CON, control.

Fig 1 Illustration of a single-user training with the radiological VRsimulator. During actual training, workgroups of three studentscollaborated in-front of these screens.

Ange andring i proojekion mellan bild (1), (2), och (3).

Lagesbestam mesiodensen i forhallande till angransande tanders rotter.

Fig 2 Example of an MS PowerPoint slide from the CON grouptraining.The textual feedback given in this image read as follows. Line 1states the alteration in projection between image (1), (2), and (3).Line 2 locates the mesiodens in relation to the roots of adjacentteeth.



‘exposed’ at will by students, immediately presented bythe simulator as geometrically correct radiographs ren-dered from the positions of the models. Four types ofexercises centred on object localization were included.In the fourth exercise, it was possible to view the two-dimensional X-ray image change in real time as themodel was manipulated. For further technical specifica-tions and discussion of validity, see Nilsson (2007).

Video-recorded observation

Like many other researchers of health care education(e.g. Hindmarsh 2010; Koschmann et al. 2010) andeducational health care simulations (e.g. Rystedt &Lindwall 2004), we have used video-based observationsto capture learning in practice. To enable analysis andcomparisons of group interaction during training, thetraining sessions were recorded using a DV camera.Twelve groups meant twelve one-hour recordings. Thecamera was placed so that the upper half part of the stu-dents was visible while neither the computer screennor the teacher was visible.

The analysis of group interaction during training wasperformed through two phases. In phase one, threequestions were posed to a number of randomly chosenvideo recorded training sessions. These questionsresulted in detailed descriptions from which thematiccategories of group interaction were inferred. Thesequestions were, [W]hat are the participants’ talkingabout? How are they talking about it? How do theyrelate to each other and to the learning environment as awhole? When no more categories were found, i.e. satu-ration had been reached, phase one ended. In phasetwo, all video data was split into one-minute timesegments and coded with the previously abstractedthematic categories. In our descriptive presentationsof the observations (such as Tables 2 and 4, or Figs 3and 4), these time-segments are our empirical unit ofobservation. The categories were limited to whetherthe content of the conversation was action-oriented,interpretive, technology-focused, meta-reflective, theo-retical or social; whether or not subject-specific termi-nology applied; which member was manoeuvring thesimulator; which members were active in the conversa-tion; which, if any, member had command over theverbal space. This allowed us to conduct a highly struc-tured analysis based on an understanding that wasinfluenced by the current set of data.

The coding of video recorded sessions was per-formed by one of the researchers. In order to producea measure of the coding stability (Krippendorff2004), one of the sessions was re-coded, by the sameresearcher, and compared with the original for each cat-egory described above. The per cent agreement betweenoriginal coding and re-coding was 97% for content,92% for terminology, and 98% for manoeuvre, verbalspace, and verbal activity, respectively.

Follow-up interviews

After an initial analysis, follow-up interviews were per-formed with one half (18) of the participants. The aimwas to better understand the training experience in SIM,how it differs from CON and to better understand theobserved differences between the two. With open-endedquestions, we talked about the respondent as a learner,training impact on learning, the respondent as a groupmember, the tasks, realism and functionality. Theseinterviews included 18 participants in total, nine fromthe respective group. They were performed individuallywith an effective time usage of 30–50 min, during aperiod of 2 weeks. Video recordings of the respondents’training session were played on a laptop computer tosupport recall. All interviews were recorded on tape. Aqualitative approach was adopted in the analysis focus-ing on inferring categories of responses, sometimescalled meaning concentration (Kvale & Brinkmann2009), rather than a quantitative count of how many saidwhat, i.e. we posed specific questions to each transcript,extracted the responses related to it, and inferred catego-ries of ideas from these responses. The end product isillustrated in Table 2, where categories of respondentideas about reasons for using subject-specific terminol-ogy is presented along with quotes that fall into each cat-egory. A selection of results will be presented as thedifferent categories of responses identified within theSIM group or in the SIM and CON groups. Quotes ofstudent responses have been translated from Swedishinto English.

Results and analysis

Application of subject-specific terminology

As previously mentioned, there was a significantdifference between SIM and CON with regard to the



frequency with which they applied a subject-specificterminology as opposed to subject-non-specific termi-nology (Söderström et al. 2008). The question we posehere is, what causes the extent of application of subject-specific terminology to differ between groups workingwith the ECAS (SIM) and groups working convention-ally (CON)?

The first step was asking the students themselves,through interviews, why they were using the subject-specific terms during training. As a group, the studentspresented a few thematic reasons, not all of them com-patible with one another, for using these terms. Theseare presented in Table 2 and include (1) The termsenable communication; they are the only words theyknow (for projection and anatomy). (2) Students allshare the same understanding of the terms. (3) Theterms make communication easier and more efficient.The alternative, using everyday words, would be morecomplicated. (4) Teachers have previously encou-raged students to use them. (5) The solution to thetasks is supposed to be presented in these terms(CON only).

When trying to understand what caused differencesbetween SIM and CON, there are two particularlyinteresting themes in these responses. The first is theidea put forward in CON that the solution was sup-posed to be presented in subject-specific terms (4). Asone of the CON students puts it, [example (5) in

Table 2], ‘Because in the answer you are supposed torespond, this is more distal or more mesial. That makesit easier if you communicate in that way from thebeginning’.

It was, however, not the case that the students wererequired to produce a solution in the subject-specificterms. The students in the CON groups were, however,required to describe changes in projection betweenimages and to describe the relation among certainobjects. As indicated by the responses, the studentsfound it much easier to do so by using terms forprojection and anatomy than by using everyday lan-guage. However, the feedback after each task was inCON, the correct solution if you will, given as one-sentence descriptions in subject-specific terms. Thissuggests that for the students, the correct answer maytend to be identical to the one written by the teacher.The SIM students were also required to interpretprojection and locate objects but, and this is a signifi-cant difference, they were tasked to manoeuvre themodel based on this interpretation. And the feedbackthey were given is in part visual, a correct positioningof the model was shown, and numerical, the distance orangle between the students’ position and the correctposition was shown in numbers. If the medium offeedback impacts the process, then this would explainpart of the difference in the application of subject-specific terminology. It would also indicate that if

Table 2. Categorization of respondents’ answers to the question of why they use the academic terminology during training.

Inferred category Example quote Present in group

(1) Terms enablecommunication

(1) We can’t just say that this tooth is . . . we have to say that it is distalon this tooth. That is the term we have learned for that trajectory.

Both

(2) These are the only words we have. It would not work withoutthem.

Both

(2) Students shareunderstanding of theterms

Because we have learned them all three of us know what we aretalking about, because it is correct. If we say buccal everyone in thegroup understands.

Both

(3) Terms makecommunication efficient

It is easier to work with my classmates if we can use these terms. Thereis a lot of anatomy we have to learn.

Both

(4) Prior teachers’ orders We wouldn’t have if the teachers hadn’t forced us to start using them.It is the language you are supposed to use when working andwriting.

Both

(5) The solution is to bepresented in these terms

Because in the answer you are supposed to respond: this is more distalor more mesial. That makes it easier if you communicate that wayfrom the beginning.

CON

n = 18.CON, control.



SIM students were given textual feedback similarto that in CON, then there would be an increase in theiruse of these terms.

The second thing to note in the responses is that thesubject-specific terms were associated with anatomyand projection, as illustrated here by the examplequotes for (1) and (3) in Table 2. In other words,these terms were especially relevant when tryingto interpret changes in projection and to locateobjects. They were not necessarily central for manoeu-vring the simulator. This suggested that there shouldbe a positive relationship in the observational databetween what students talked about and the terminol-ogy they used for talking about it. As illustrated byTable 3, such relations do exist. When the content ofthe verbal interaction was interpretations, SIM studentsapplied subject-specific terms to a much greater extent,as compared to when the content was action-oriented(64.5% of cases compared to 11.4%, chi square ª 93.8;P < 0.001).

This means that the differences in tasks between SIMand CON, which encourage more focus on action forSIM, impact the application of subject-specific termi-nology. However, when comparing the interpretivecontent of SIM and CON, it is apparent that CON

students still apply subject-specific terms much moreoften (90.5% of all interpretive instances compared to64.5% for SIM, chi square ª 46.4; P < 0.001). We con-clude that while differences in tasks impact the extent ofapplication of subject-specific terminology, this doesnot explain all of the difference between the SIM andCON. We suggest that the medium of feedback is alsoimportant.

The second question for this theme is, what, if any,implications does the extent of application of subject-specific terminology have for proficiency development?Table 4 illustrates how often SIM groups appliedsubject-specific terminology (as the percentage of theobservational time-segments in which they occur), inrelation to group proficiency development. It revealsthat the group that most often applies subject-specificterms, SIM 2, also develops most (14 points); it alsoreveals that the group that most rarely applies theseterms, SIM 4, develops the least (-2 points). These twocases do, however, seem to be exceptions to a pattern ofrandomness, because there is no relation between thetwo factors in the remaining four groups (Spearmansr ª 0.6; P > 0.05). We conclude that the extent of appli-cation of subject-specific terminology have little directimpact on proficiency development.

Table 3. Comparison of how verbalcontent and applied terminologyco-appear for SIM and CON groups.

Training Verbal content Subject-specificterminology

Not subject-specificterminology

CON Interpretation 90.5% 9.5%Action-oriented 10% 90%

SIM Interpretation 64.5% 35.5%Action-oriented 11.4% 88.6%

Percentage based on the total number of observational time segments where thecategory is identified.CON, control; SIM, simulator.

Table 4. Extent of application of subject-specific terminology in relation to groupproficiency development, divided by SIMgroup.

Group Subject-specific terminology Group proficiency development

SIM 1 41% 2SIM 2 46% 14SIM 3 37% 10SIM 4 10% -2SIM 5 28% 9SIM 6 30% 3

Percentage based on the total number of time segments for the respective group.SIM, simulator.



Student-simulator interaction affordances –manoeuvring the simulator

Are there distinguishable patterns for how studentsshare control over the simulator?As illustrated in Fig 3,the observations revealed two types of collaborationaround the student-simulator interaction. The first one,illustrated by SIM 1, SIM 2, SIM 4, and SIM 5, is char-acterized by manoeuvral turn-taking. Each groupmember got a chance to manoeuvre the simulator, albeitnot for an equal amount of time. The second one, illus-trated by SIM 3 and SIM 6, is characterized by manoeu-vral dominance where one of the members operated thesimulator almost all the time. A common characteristicof the manoeuvral dominance groups was that themember sitting in the middle, right in front of the com-puter, was the one manoeuvring the simulator. Whenasked, through interviews, if the choice of seat was con-

scious, the dominant female operating the simulator inSIM 6 claimed, ‘I decided to sit in the middle . . . I wantto have control and take command’. Most studentsclaim, however, that it was not conscious. We concludethat SIM groups differ in their sharing of control overthe simulator and that there are two distinguishable col-laborative patterns, turn-taking and dominance.

Do the patterns of control over the simulator on thegroup level, or time spent manoeuvring the simulator onthe individual level, directly impact proficiency devel-opment? As illustrated by Table 5, there are better andworse developments for groups characterized by turn-taking (2, 14, -2, 9 points of development, respectively)as well as dominance (10, 3 points of development,respectively). On the individual level, there seems to beno clear-cut relationship between time spent manoeu-vring the simulator and proficiency development(Spearmans r ª 0.1; P > 0.05). The operators in the

Fig 3 Illustration of the time spent by each group member manoeuvring the simulator.Members of each group are labelled after their position in front of the computer: left, middle, or right.



dominance groups, SIM 3 and SIM 6, developed a littlemore than their next-best group member (2 and 1 pointsbetter, respectively). And the primary operators in theturn-taking groups developed equally (SIM 2) or less(SIM 1, 4, 5) in comparison to their group members. Inone of the groups, SIM 5, the member with the leastamount of manoeuvring-time developed much morethan her peers (7 points in comparison to 2 and 0). Weconclude that neither the patterns of control over thesimulator nor the time spent manoeuvring the simulatordirectly impact proficiency development.

Do specific patterns of control over the simulatorproduce specific patterns of verbal interaction, anddoes individual control over the simulator also entailcontrol over the verbal interaction? Figure 4 illustrateshow often a given member has command over the verbalspace in the respective SIM group. Beginning with theturn-taking SIM 1, the verbal pattern was significantlydifferent from the manoeuvral pattern with one persondominating the verbal space throughout the trainingsession. In the similar turn-taking group SIM 5, a differ-ent pattern appears, a distinct dyadic pattern with twomembers in tight communication. Moving to SIM 4, amanoeuvral dominance group, we find that the memberthat operated the simulator throughout the training

session rarely took command of the verbal space. But inthe similar SIM 6, the dominant operator also took clearcommand of the verbal space. We conclude that specificpatterns of control over the simulator do not producespecific patterns of verbal interaction. Nor does indi-vidual control over the simulator also entail controlover the verbal interaction. We find two patterns ofverbal interaction, one dyad-ish and one based ondominance.

Are the verbal patterns or individual control over theverbal interaction related to proficiency development?Regarding SIM 2, 3, 4, 5 as variations of a dyadicpattern with one more or less dominant member, andSIM 1 and 6 as variations of a dominance pattern, thereis no clear-cut relation between verbal pattern and pro-ficiency development. Developments in the dominancegroups were weak (2, 3 points, respectively), and some-what varied in the dyad-ish groups (14, 10, -2, 9,respectively). The fact that SIM 1 and SIM 6 were char-acterized by having one particularly strong memberwho developed very little with regard to proficiencytest scores (1 and 2 points, respectively) would alsosuggest that dominance does not ensure the individual’sdevelopment, and also seems to do little to help the othermembers. However, because the verbally shy member

Table 5. Manoeuvral patterns and proficiency test scores and development for SIM groups and individual SIM group members.

Group Man. pattern Member* Pre test score Post test score Development Group dev.**

SIM 1 Turn-taking Left 19 19 0Middle 15 16 1Right 15 16 1 2


SIM 3 Dominance Left 12 15 3Middle 13 18 5Right 11 13 2 10

SIM 4 Turn-taking Left 7 7 0Middle 18 15 -3Right 14 15 1 -2


SIM 6 Dominance Left 16 17 1Middle 10 12 2Right 13 13 0 3

*Group members are described by their physical position in front of the simulator.**Group dev. is the combined development score for members of a given group.SIM, simulator.



in SIM 5 gained so much (7 points), the data gives noclear-cut support for the opposite either, that mutualregulation of the verbal space is essential for collabora-tion to be effective with regard to proficiency develop-ment. No significant correlation between individualcontrol over the verbal space and proficiency develop-ment was found (Spearmans r ª -0.05; P > 0.05). Weconclude that neither the verbal pattern nor individualcontrol over the verbal interaction is directly related toproficiency development.

How is the group collaboration around the student-simulator interaction perceived by the students them-selves? The interviews showed that the SIM students areaware that some members spend more time operatingthe simulator and that some members have more influ-

ence on the verbal interaction. Table 6 illustrates six cat-egories of factors put forward by students to explainthese differences. (1) Personality: some people want tolead and others do not, and this determines a person’sbehaviour in the specific collaboration. (2) Social rela-tions: two members are more familiar with each otherencouraging a dyad-ish interaction. (3) Competence:members’ subject competence varies and this decideshow central they are to the collaboration. (4) Techno-logical: position in front of the computer determineshow central members are to the collaboration. (5)Chance: members’ behaviour varies from day to daydue to factors such as fatigue. Similar topics were raisedwhen the students were asked to make suggestionsfor how to support group collaboration. We conclude

Fig 4 Illustration of how often group members dominate the verbal space.Members of each group are labelled after their position in front of the computer: left, middle, or right.



that the students are aware of the unequal distri-bution of control over the simulator and the verbalspace. They posit four categories of explanations as towhy this occurs, individual, social, technological, andcontextual.

Summary and discussion

This article has contributed to the investigation of col-laboration as a technique for using ECAS in health careeducation. We have focused on relations between tools,collaboration and learning, and specifically on twothemes. First, causes and effects of the extent of applica-tion of subject-specific terminology. Second, patterns

of collaboration around the student-simulator interac-tion and the effects of these patterns on proficiencydevelopment.

Observing SIM and CON groups side by side origi-nally made it obvious that students’ training conven-tionally made use of subject-specific terminology to amuch greater extent than students working with theECAS. In this article, we have argued that these termsare part of cultural tools associated with academicdomains of anatomy and projection, and that these aremore tightly associated with interpretive activity as con-trasted with the action-oriented activity that is requiredby simulation tasks. This suggests that the differencesare, in part, explained by differences in what the stu-dents are tasked to achieve. We argue, however, that thedifferences in tasks do not explain all the differencein applications of subject-specific terminology andpropose that the medium of feedback may also be part ofthe explanation. While SIM students were given visualand numeric feedback, CON students were giventextual feedback in subject-specific terms. If thishypothesis is true, that a certain characteristic of thetools, the medium of feedback, contribute to theseeffects, then SIM students would increase their applica-tion of subject-specific terminology if given textualfeedback similar to that given in CON. Further researchmay yield more information about how different mediaof feedback after subtask completion impacts the col-laborative process and learning outcomes. We have,however, found no clear-cut relation between the extentof application of subject-specific terminology andproficiency development within the SIM groups. It ispossible that a more qualitative analysis, following thestudents’ problem-solving processes, is necessary tomake useful associations between collaborative pat-terns of tool usage and proficiency development. It isalso possible that collaborating extensively withsubject-specific terms has other benefits than what isevaluated by proficiency tests such as the one used inthis study.

When training with an ECAS, interaction with thesimulator is essential, and depending on the type ofsimulator, the hands-on physical interaction may bemore or less central to the collaboration. We studiedhow the physical student simulator-interaction affor-dances impact collaboration and proficiency develop-ment. Observations of the SIM groups made it obvioushow control over the simulator is shared and not shared,

Table 6. Categorization of respondent (SIM only) statementsabout differences in time spent manoeuvring the simulator andverbal space.

Inferred category Example quote

(a) Personality 1) I am one of those that are notafraid to speak my mind, and I canbe completely wrong. I am veryactive in classes too, ask a lot, I’mnot someone to sit quiet and think.Me and X talk much more, but I seethat as natural, that’s just the way itis, she is much less talkative thanme and X.

2) I don’t want to be the one leadingthe discussion, or lead anything atall. Maybe I rather listen and thenI’ll speak if something gets strange.Better to let those lead who wantto lead.

(b) Socialrelations

But also that I and X are pretty familiarwith each other. We already kneweach other well.

(c) Competence 1) X is very good at these things, soone tends to trust him.

2) I’m the one that in most cases takesthe initiative in presentations,transcriptions etc. It is probablybecause if I do it, it is donecorrectly.

(d) Technological I also think that you get to hold itmore if you sit in the middle.

(e) Contextual It also depends on what day it is, if youare tired etc. Some days you justwant to listen.

n = 9.SIM, simulator.



but it is harder to know if and how this impacts learning.We have shown that there are two distinct collaborativepatterns for sharing control over the simulator. One wascharacterized by turn-taking, albeit with differentdegrees of equality, and the other by one-member domi-nance. Both turn-taking and dominance patternsproduce both better and worse group proficiency devel-opment, suggesting that neither pattern is in itself better.In the two dominance groups, being the sole operatormay have been slightly beneficial, but in the turn-takinggroups the impact on proficiency development wasvaried.

We also studied whether these two patterns producedspecific patterns of verbal interaction. Observationsrevealed differences in how the verbal space is distrib-uted among group members. We found a dominancepattern, where one of the members has command overthe verbal space almost all the time. We also foundvariations of a dyad-ish pattern where a somewhatdominant member shares more or less space with one ofthe other members while tending to exclude the third.There was, however, no clear-cut relationship betweenthe patterns of control over the simulator and the pat-terns of control over the verbal space. Nor was there aclear-cut relationship, on the individual level, betweencontrolling the simulator and controlling the verbalspace. We also found no relationship between profi-ciency development and verbal pattern, or individualcontrol over verbal space. Further research may give usdeeper insights into if and how these collaborative pat-terns, in other respects, impact collaborative learningwith ECAS in health care education. This is, however,an illustration of Säljö’s suggestion that technologies‘have implications for social activities, some of whichmay be foreseen and others which materializes as thetechnology becomes integrated into specific activities’(2010, p. 54).

The students themselves were aware of the differ-ences in the distribution of verbal space and control overthe simulator. When trying to explain it and when sug-gesting how collaboration can be optimized, they namefactors such as personality, social relationships, compe-tence, position in front of the computer and chance. Thefact that students have ideas about whom they think theywould collaborate well with lead to ideas about student-regulated matchmaking and group creation mecha-nisms. According to Wessner and Pfister (2001),‘[G]roup formation in CSCL environments is either

done manually with little support from the system, or thesystem needs an elaborated model of the learningdomain in order to select potential peer learners and toform learning groups in a pedagogically sound way.’Because our students had studied together as a class forabout 2 years, it would have been interesting to knowhow the collaborative training with this VR simulatorwould have been affected by such a simple thing ashaving students create their own groups. Would they inpractice have teamed up with individuals that enhancedcollaboration, or just made the training more social?Then again, maybe there is a professional value in stu-dents continuously learning to collaborate with differentindividuals irrespective of their personal differences?Either way, the social composition along of groups isone important aspect to consider, along with the collabo-rative activities and the technological support, whendesigning CSCL environments (Stahl 2004, p. 87)

As with any scientific method, we set limitations forthe generalizations possible to make from the empiricaldata, and some of them need to be mentioned. One ofthese limitations is that the comparisons made on grouplevel are made with a relatively small number of units,six. This means that we need to be cautious in makingstatistical generalizations from these comparisons.They do, however, serve a purpose as indicators of thestate of our simulation training. This is why we havegiven descriptive statistics a prominent role whendealing with relationships between group level patterns.The limitations in space have also required us to focuson some issues and leave others for future papers, suchas further analysis of the interrelationships betweenfactors considered in this text. It is possible for instancethat there is a positive correlation between applicationof subject-specific terminology and proficiency devel-opment but that this effect is countered by factors suchas group interaction patterns. Introducing simulatorlogs of student activity, such as time on task and perfor-mance on tasks could also enrich our understanding ofthe matter.

One final thing must be noted about the value of col-laboration. The mean proficiency development for thecollaborative SIM groups in this study, a significanttwo-point development, is identical to that found byNilsson et al. (2007) for comparable students trainingindividually with the same radiological VR simulator.That is, having students train individually and havingthem work collaboratively in groups of three, in the



manner they did in this study, seem to make no differ-ence with regard to the group’s mean proficiency devel-opment. We may be able to enhance our support forcollaboration by identifying beneficial collaborativepatterns and encouraging these during training as Dil-lenbourg et al. (2009) suggests, thereby supporting stu-dents’ proficiency development. But, are we not equallylikely to be able to enhance support for individual train-ing to make that more efficient? Is this a type of screen-based simulation training, and training with ECAS ingeneral, activities where the answer to the ‘how do weoptimize training efficiency’ question should generallybe answered by collaborative activity? These questionsare related to the questions of what we want to achievewith education and what part of this we can, and do,evaluate with student examinations. Some would saythat collaboration has benefits other than that of learn-ing to solve the task at hand, like developing criticalthinking (e.g. Gokhale 1995), and that it is necessaryto achieve certain learning goals (Kirschner et al. 2004).As long as ECAS continues to spread in health care edu-cation, be it because of a paradigm shift in educationalmodels or because of other factors, research needs tohelp us better understand how to support students whentraining with them, irrespective of whether the trainingis done collaboratively or individually.

Note

1For topological discussions of ECAS in health care see examples for Gaba

(2004) and Nehring and Lashley (2009).

References

Abrahamson S., Denson J. & Wolf R. (1969) Effectiveness ofa simulator in training anesthesiology residents. Journal ofMedical Education 44, 515–519.

Bradley P. (2006) The history of simulation in medical educa-tion and possible future directions. Medical Education 40,254–262.

Chopra V., Gesink B.J., De Jong J., Bovill J.G., Spieerdijk J. &Brand R. (1994) Does training on an anaesthesia simulatorlead to improvement in performance? British Journal ofAnaesthesia 73, 293–297.

Crook C. (1994) Computers and the Collaborative Experi-ence of Learning. Routledge, London.

Cuban L. (2001) Oversold and Underused: Computers in theClassroom. Harvard University Press, Cambridge, MA.

Dillenbourg P., Järvelä S. & Fischer F. (2009) The evolution ofresearch on computer-supported collaborative learning.

In Technology Enhanced Learning (eds N. Balacheff, S.Ludvigsen, T. de Jong, A. Lazonder & S. Barnes), pp. 3–19.Springer, Berlin.

Engum S.A., Jeffries P. & Fisher L. (2003) Intravenous cath-eter training system: computer-based education versustraditional learning methods. The American Journal ofSurgery 186, 67–74.

Enyedy N. (2003) Knowledge construction and collectivepractice: at the intersection of learning, talk, and social con-figurations in a computer-mediated mathematics class-room. Journal of the Learning Sciences 12, 361–407.

Gaba D.M. (2004) The future vision of simulation in healthcare. Quality and Safety in Health Care 13 (Suppl. 1), 2–10.

Gokhale A.A. (1995) Collaborative learning enhances criticalthinking. Journal of Technology Education 7, 22–30.

Hindmarsh J. (2010) Peripherality, participation and commu-nities of practice: examining the patient in dental training.In Organisation, Interaction and Practice (eds N. Llewel-lyn & J. Hindmarsh), pp. 218–240. Cambridge UniversityPress, Cambridge.

Issenberg S.B., McGaghie W.C., Petrusa E.R., Lee Gordon D.& Scalese R.J. (2005) Features and uses of high-fidelitymedical simulations that lead to effective learning: a BEMEsystematic review. Medical Teacher 27, 10–28.

Kirschner P.A., Martens R.L. & Strijbos J.W. (2004) CSCL inhigher education? A framework for designing multiplecollaborative environments. In What We Know about CSCL– and Implementing It in Higher Education (eds J.W.Strijbos, P.A. Kirschner & R.L. Martens), pp. 3–30. KluwerAcademic Publishers, Boston.

Koschmann T., ed. (1996) CSCL: Theory and Practice of AnEmerging Paradigm. Lawrence Erlbaum, Hillsdale.

Koschmann T., LeBaron C., Goodwin C. & Feltovich P.(2010) ‘Can you see the cystic artery yet?’A simple matterof trust. Journal of Pragmatics 43, 521–541.

Krippendorff K. (2004) Content Analysis. An Introduction toIts Methodology. Sage, Thousand Oaks, CA.

Kvale S. & Brinkmann S. (2009) Den kvalitativa forskn-ingsintervjun. [The Qualitative Research Interview]. Stu-dentlitteratur, Lund, Sweden.

Lazonder A.W., Hagemans M.G. & de Jong T. (2010) Offeringand discovering domain information in simulation-basedinquiry learning. Learning and Instruction 20, 511–520.

Luengo V., Aboulafia A., Blavier A., Shorten G., Vadcard L. &Zottmann J. (2009) Novel technology for learning in medi-cine. In Technology Enhanced Learning (eds N. Balacheff,S. Ludvigsen, T. de Jong, A. Lazonder & S. Barnes),pp. 105–120. Springer, Berlin.

McGaghie W.C., Issenberg S.B., Petrusa E.R. & Scalese R.J.(2010) A critical review of simulation-based medical edu-cation research: 2003–2009. Medical Education 44, 50–63.



Mercer N. (2005) Sociocultural discourse analysis: analysingclassroom talk as a social mode of thinking. Journal ofApplied Linguistics 1, 137–168.

Nehring W.M. & Lashley F.R. (2009) Nursing simulation: areview of the past 40 years. Simulation & Gaming 40, 528–551.

Nilsson T.A. (2007) Simulation supported training in oralradiology. Methods and impact in interpretative skill. (Doc-toral dissertation, Umeå University).

Nilsson T.A., Hedman L.R. & Ahlqvist J.B. (2007) Arandom-ized trial of simulation-based versus conventional trainingof dental student skill at interpreting spatial information inradiographs. Simulation in Healthcare 2, 164–169.

O’Malley C. (1995) Computer Supported CollaborativeLearning. Springer, Berlin.

Ovasapian A., Yelich S.J., Dykes M.H. & Golman M.E.(1988) Learning fiberoptic intubation: use of simulators v.traditional teaching. British Journal of Anaesthesia 61,217–220.

Peugnet F., Dubois P. & Rouland J.F. (1998) Virtual realityversus conventional training in retinal photocoagulation: afirst clinical assessment. Computer Aided Surgery 3, 20–26.

Pilkington R.M. (1998) Dialogue games in support of qualita-tive reasoning. Journal of Computer Assisted Learning 14,308–320.

Pilkington R.M. & Parker-Jones C.H. (1996) Interacting withcomputer-based simulation: the role of dialogue. Comput-ers and Education 27, 1–14.

Quinn F., Keogh P., McDonald A. & Hussey D. (2003) Astudycomparing the effectiveness of conventional training andvirtual reality simulation in the skills acquisition of juniordental students. European Journal of Dental Education 7,164–169.

Rystedt H. & Lindwall O. (2004) The interactive constructionof learning foci in simulation-based learning environments:a case study of an anaesthesia course. PsychNology Journal2, 165–188.

Säljö R. (1999) Learning as the use of tools: a socioculturalperspective on the human-technology link. In Learningwith Computers (eds K. Littleton & P. Light), pp. 144–116.Routledge, London.

Säljö R. (2000) Lärande i praktiken: ett sociokulturellt pers-pektiv [Learning in Practice: A Sociocultural Perspective].Prisma, Stockholm.

Säljö R. (2010) Digital tools and challenges to institutionaltraditions of learning: technologies, social memory and theperformative nature of learning. Journal of ComputerAssisted Learning 26, 53–64.

Söderström T., Häll L.-O., Nilsson T. & Ahlqvist J. (2008)How does computer based simulator training impact ongroup interaction and proficiency development. Proceed-ings of the International Conference of Information Com-munication Technologies in Education Corfu, Greece,10-12 July, 2008.

Stahl G. (2004) Group cognition in computer-assisted col-laborative learning. Journal of Computer Assisted Learning21, 79–90.

Wertsch J.V. (1998) Mind As Action. Oxford University Press,NewYork.

Wessner M. & Pfister H. (2001) Group formation in computer-supported collaborative learning. In Proceedings of the2001 International ACM SIGGROUP Conference on Sup-porting Group Work (eds S. Ellis, T. Rodden & I. Zigurs),Sep 30–Oct 3, 2001, Boulder CO, pp. 24–31. ACM, NewYork.



Copyright of Journal of Computer Assisted Learning is the property of Wiley-Blackwell and its content may not

be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written

permission. However, users may print, download, or email articles for individual use.

Documents

Colaborative Learning in Education