Taking a Scientific Approach to ScienceEducation, Part I–ResearchDeveloping expertise requires intense practice that includes doing challengingand relevant tasks, followed by feedback and reflection on one’s performance

Carl Wieman and Sarah Gilbert

During the past few decades, major advances inthe fıelds of cognitive psychology, brain research,and discipline-based education research incollege science classrooms are providing guidingprinciples for how to achieve learning of complexknowledge and skills such as science. In part I, wedescribe the nature of expertise and how it islearned, primarily based on the fındings of cogni-tive psychology. We also give examples of studiesin undergraduate science classrooms and the re-sulting student outcomes compared with thosefrom traditional lecture instruction.

In the second feature of this two-part series, wewill discuss the challenges and opportunities formaking these teaching methods standard prac-tice in undergraduate science classrooms and theresults of a large-scale successful experiment indoing so.

The Nature and Learning of Expertise

Learning to think about and use science more likea scientist who is already working in the disci-pline is a primary educational goal for most un-dergraduate science courses. But exactly what ismeant by “thinking like a scientist”—in otherwords, what is scientifıc expertise? Cognitive psy-chologists have extensively studied expertiseacross a variety of disciplines, including history,science, and chess. They fınd three componentsthat are common to all fıelds:

• large amounts of specialized knowledge• a specifıc mental organizational framework,

unique to the fıeld of expertise• the ability to monitor one’s own thinking and

learning in the fıeld of expertise

Although the fırst component is no surprise,knowing lots of information is not useful if a

person cannot quickly recognize how that infor-mation can solve a particular problem. Expertsorganize information in unique discipline-spe-cifıc frameworks for effıcient and accurate re-trieval and application. This practice entailsgrouping information according to certain com-plex patterns and relationships. Much of whatare called scientifıc concepts are the way thatexperts in a fıeld of science link lots of informa-tion within a single category, thus allowing themto decide quickly where that information is rele-vant.

The third general characteristic of expertise isthe ability to monitor one’s own thinking. Whenworking on a problem, a scientist is regularlyasking: Is this approach working? And do I reallyunderstand this? Experts have the resources toanswer those questions and modify what they aredoing accordingly.

Research indicates that everyone requiresmany thousands of hours of intense practiceto reach a high level of expertise. This require-ment to spend so much time in developing exper-tise is set by biology. The brain changes throughthis intense practice, and is rewired to build theseexpert capabilities. Much as a muscle develops in

SUMMARY

➤ To acquire expertise, one must develop a large body of specializedknowledge, a specific framework for that knowledge, and a capacity tomonitor his or her own thinking about that field.

➤ Teachers need to have mastery of a field of expertise and convey theimportance and excitement of that field.

➤ Many active learning approaches achieve greater learning than conven-tional lectures.

➤ Preliminary findings indicate that it is better to delay the use of jargon inclasses until after students are introduced to the relevant concepts.

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response to prolonged intense exercise, the brainresponds to intense “mental exercise.”

In addition to identifying generic componentsof expertise, cognitive psychology research alsoidentifıed a common process required for devel-oping expertise, called deliberate practice. It in-volves many hours of intense practice, but thatpractice must have very specifıc characteristics. Itmust involve tasks that are diffıcult for learners,requiring their full focus and effort to achieve, butthat are still attainable. The tasks must also ex-plicitly practice the specifıc components of exper-tise to be learned. Finally, there must be timelyand specifıc feedback, typically from a coach orteacher, on how well a learner has done and howto improve, and then reflection by the learner onhow to use that guidance.

Components of Scientific Expertise

A few examples of specifıc components of exper-tise in any area of science include:

• recognizing and using concepts and mentalmodels and developing sophisticated selectioncriteria for deciding when specifıc models areapplicable

• recognizing relevant and irrelevant informa-tion for solving a problem

• knowing and applying a set of criteria for eval-uating if a result or conclusion makes sense

• moving fluently among specialized represen-tations such as graphs, equations, and special-ized diagrams.

We selected these particular examples becausethey are seldom practiced with feedback in typicalundergraduate science courses.

A highly effective teacher maximizes theamount and effectiveness of deliberate practiceby students. This role requires them to have sub-stantially more content expertise than does tradi-tional teaching by lecture. Teachers must havedeep expertise in their respective disciplines todesign suitable tasks that provide authentic prac-tice of expert skills for their students at the appro-priate level of challenge. The teachers also musthave substantial content expertise to provide spe-cifıc feedback on how well the students are per-forming those tasks and how they can improveperformance. Finally, since this practice is inher-ently hard work, it requires motivation. An ex-pert in the subject is uniquely positioned to helpprovide that motivation by conveying the impor-tance and excitement of the subject.

Examples of Studies on Learningin Undergraduate Science Courses

Here are several examples of studies on learningin science courses. The fırst comes from a study

FIGURE 1

Learning gains on concept inventory for introductory physics course sections taught by 9 instructors who switchedfrom traditional lecture instruction (avg. gain 0.3) to an interactive method (avg. gain 0.6). (Adapted fromHoellwarth, C., and M. J. Moelter. 2011. The implications of a robust curriculum in introductory mechanics. Am. J.Phys. 79:540 –545.)

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conducted by Chance Hoellwarth and MatthewMoelter in an introductory physics course at Cal-ifornia Polytechnic State University. Their studyinvolved many different instructors of physicsacross many sections and looked at the amount oflearning before and after these same instructorschanged their teaching methods.

Hoellwarth and Moelter used a validated andwidely used concept inventory test to measurestudent learning gains on core concepts coveredby the course. The learning gain is a measure ofthe fractional amount a student improves be-tween their pre-course score and post-coursescore on the test, with a gain of 1 meaning aperfect score on the post-test. Hoellwarth andMoelter collected such data for students for anumber of years while classes were taught usingtraditional lecture instruction (Fig. 1, the Cal PolyTrad. Avg. line). The average learning gain was abit less than 0.3, which is typical for a well-taughtlecture course on introductory physics.

Next, all the instructors switched to a “studio”approach, in which the students worked in smallgroups to carry out a common set of carefullydesigned tasks, and the instructors served as fa-cilitators/coaches. After this switch, the averagelearning gain doubled to 0.6 (Fig. 1). We want to

emphasize that this change occurred with thesame set of instructors who changed the teachingmethods that they were using, after which theirstudents learned far more of the concepts beingcovered.

Our second example is from the work of BethSimon and coauthors in Computer Science atUniversity of California, San Diego. Simon spenta year working with us and learning about theactive learning technique for teaching introduc-tory physics called Peer Instruction. This ap-proach involves regularly posing questions tostudents during classes, having them answer withclickers that record their responses, and thenhaving them discuss the material in small groupsbefore re-answering those questions.

Simon worked with six other instructors tointroduce this method in four core courses incomputer science. There was a dramatic decreasein the drop and failure rates across all fourcourses, with the overall average being about 1/3of what it was previously (Fig. 2). This fıgurerepresents a very large number of students who,only because the instructors changed their teach-ing methods, are now successfully pursuing de-grees.

FIGURE 2

Failure rates for 4 computer science courses when instructors used standard instruction vs. interactive PeerInstruction. (Adapted from Porter, L., C. Bailey-Lee, and B. Simon. 2013. Halving Fail Rates using Peer Instruction:A Study of Four Computer Science Courses. Proc. 44th ACM Technical Symposium on Computer Science Education(SIGCSE ’13), p. 109 –114.)

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Those two studies, the fırst by Hoellwarth andMoelter and the second by Simon and her collab-orators, looked at the fınal results of studentstaking full courses. However, a great deal oflearning takes place outside classrooms whiledoing homework assignments and studying forexams, for example. This raises the research ques-tion, how much difference do these research-based teaching methods make in the learning thattakes place only in the classroom, which is themain focus of most instructors’ attentions?

This classroom component of learning wasmeasured using two large sections (270 studentseach) of the introductory physics course taken byall engineering students at the University of Brit-ish Columbia, by one of us (CW) and collabora-tors L. Deslauriers and E. Schelew. Before theexperiment, the performances by students in twoseparate sections were carefully measured andseen to be nearly identical. Thus, within the smallstatistical uncertainties of such large classes, theirscores on tests of conceptual mastery, on twomidterm exams, attitudes about physics, atten-dance, and engagement in class were nearly iden-tical.

One section was taught by a senior professorwho taught this class many times with good stu-dent evaluations. Another, experimental sectionwas taught for only one week by someone with aPh.D. in physics who had limited teaching expe-rience but was trained in the principles of learn-ing and research-based teaching practices in theprogram that we ran. Both instructors agreed onthe same set of learning objectives to be coveredin the same amount of class time. The timing ofour experiment was set so that students would beunlikely to do much studying outside class duringthe week that the experiment took place.

The instructor of the experimental sectionused a number of common features of research-based teaching. Students were assigned short, tar-geted readings before class and given a quiz onthe reading. During class they were given ques-tions to answer, where they would respond withclickers or by completing worksheets. This pro-cess involved each student in individual work anddiscussions with their neighbors, during whichtime the instructor would circulate through theroom listening to those discussions. There wasconsiderable instructor talking, but predomi-nantly as follow-up discussion to the activity, notpreceding it. So in this way, students were prac-

ticing scientifıc thinking and receiving feedbackfrom their fellow students and an informed in-structor.

After the week-long experiment, the studentswere given a pop quiz at the start of the followingclass, a quiz that the instructors jointly developedto probe the mastery of the learning objectivesduring the experiment. The difference in perfor-mance between the control and experimental sec-tions is very large—an effect size of 2.5 standarddeviations—and is reflected in the entire distribu-tion moving up (Fig. 3). This result reflects whatalso is seen elsewhere, namely, these teachingmethods are not just benefıcial for a subgroup ofstudents, they are much better for all students.This broad applicability is not surprising; theteaching methods are based on research on howthe human brain learns. In this study, the averagelevel of engagement of the students was also mea-sured and, as one might expect, it was muchhigher (85%) in the experimental section than inthe control section (45%). Many other studiesshow similar results, including many in biology,according to numerous reports in the journalCBE-Life Sciences Education.

Impact of Jargon when Teaching Biology

We are involved in another study evaluating theimpact of jargon on learning in biology. Thiswork was inspired by cognitive psychology re-search studying the limits of the short-termworking memory.

In simple terms, memory can be described ashaving two components. Long-term memory hasenormous capacity and lasts for decades. The sec-ond component, short-term working memory, iswhat we use on short time scales, such as timespent in a class, to remember and process newinformation. In contrast to the long-term mem-ory, the working memory has extremely limitedcapacity, around 5–7 new items for the typicalperson. As the working memory also processesinformation, it operates analogously to a PC withvery little RAM. The more it is called up to re-member and process, the less effectively it canfunction.

Many studies show that anything that in-creases demands on the working memory unnec-essarily during a learning activity reduces learn-ing. We, with Lisa McDonnell and Megan Barker,designed an experiment to test if reducing theamount of jargon introduced in a biology class

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would improve learning of the biology concepts.Although the study is ongoing, preliminary re-sults show large benefıts from introducing rele-vant jargon only after students are introduced tothe concepts.

These are just a few examples. As shown in ameta-analysis by Scott Freeman and coauthors,there is a vast literature of similar studies acrossthe science and engineering disciplines, provid-ing overwhelming evidence that interactiveteaching approaches are much more effectivethan conventional lectures at achieving learningof complex subjects.Carl Wieman holds a joint appointment as Professor of Physicsand the Graduate School of Education at Stanford University,Stanford, Calif., and Sarah Gilbert is a senior advisor at the CarlWieman Science Education Initiative, University of BritishColumbia, Vancouver, Canada. This feature is based in part ona talk given during the 2014 ASM Conference forUndergraduate Educators.

Suggested Reading

Ambrose, S., M. Bridges, M. DiPietro, M. Lovett, andM. Norman. 2010. How learning works: seven re-

search-based principles for smart teaching. Wiley, SanFrancisco, Calif.

Carl Wieman Science Education Initiative (CWSEI).2015. Resources, references, effective clicker use book-let, and videos. www.cwsei.ubc.ca/resources.

Colvin, G. 2008. Talent is overrated: what really separatesworld-class performers from everybody else. Portfo-lio, New York.

Crouch, C. H., J. Watkins, A. P. Fagen, and E. Mazur.2007. Peer instruction: engaging students one-on-one, all at once. In E. F. Redish and P. J. Cooney(ed.), Research-based reform of university physics.American Association of Physics Teachers, CollegePark, Md.

Deslauriers, L., E. Schelew, and C. Wieman. 2011. Im-proved learning in a large enrollment physics class.Science 332:862– 864.

Freeman, S., S. L. Eddy, M. McDonough, M. K. Smith,N. Okoroafor, H. Jordt, and M. P. Wenderoth. 2014.Active learning increases student performance in sci-ence, engineering, and mathematics. Proc. Natl. Acad.Sci. USA 111:8410 – 8415.

National Research Council. 2012. Discipline-based edu-cation research: understanding and improving learn-ing in undergraduate science and engineering. Na-tional Academies Press, Washington, D.C.

FIGURE 3

Test scores for students taught an introductory physics module using standard instruction vs. students taughtusing interactive engagement techniques. Random guessing would produce a score of 3. (Adapted from L.Deslauriers et al., Science 332:862– 864, 2011.)

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