Constructivism, Computer exploratoriums, and Collaborative learning: Constructing Scientific Knowledge

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Constructivism,Computerexploratoriums, andCollaborative learning:Constructing ScientificKnowledgeJohn R. Jungck aa Beloit CollegePublished online: 28 Jul 2006.

To cite this article: John R. Jungck (1991) Constructivism,Computer exploratoriums, and Collaborative learning: ConstructingScientific Knowledge, Teaching Education, 3:2, 151-170, DOI:10.1080/1047621910030218

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CONSTRUCTIVISM,COMPUTEREXPLORATORIUMS, ANDCOLLABORATIVELEARNING:

CONSTRUCTING SCIENTIFIC KNOWLEDGE1

John R. Jungck, Beloit College

ADICHOTOMY IN SCIENCE EDUCATION HAS EXISTED EVER SINCE THE SIXTIES

movement to incorporate relevance into the science curriculum. Inissues courses where the problems are framed in a social, political, ethical,and personal context, students quickly learn that their perspectives areimportant referential frames for decision making. Thus, the "real" problemsof pollution, tropical deforestation, genetic counseling and screening, AIDSprevention, greenhouse effect, and ozone layer disruption are seen as arenaswhere personal actions and societal beliefs will greatly affect the problems'"solution."

In sharp contrast, textbook presentations of "problems" in these sameareas are perceived as decontextualized from both the students' immediateexperience and values. Furthermore, instead of conceiving of "solutions" ashypotheses (Collins, 1986), students have been socialized to seek uniqueanswers such as those usually reinforced in the back of textbooks.

This dislocation between relevant problems and academic exercisesreifies the traditional notions that students often bring to science learning:namely, that "real" science is objective, value-free, and truth seeking. Thus,when authors such as Elizabeth Fee (1981) ask, "Is Feminism a Threat toScientific Objectivity?" students perceive that such epistemological debatesare about the class of socially situated problems discussed in the issues

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TEACHING EDUCATION 1991

sections of courses, but not in "real" science courses where problem solvingis predominantly algorithmic and where normative, "objective" sciencetextbooks dominate. The canon remains unchallenged in significant waysin both alternative and progressive classrooms. Students typically interpretthe existence of excellent case studies (e.g., Haraway, 1989; Keller, 1983;Latour & Woolgar, 1986; Brannigan, 1979; Fleck, 1979) as aberrantbehavior by scientists rather than standard operating procedures.

On the other hand, ethnographers (Knorr-Cetina, 1983) who record"normal" scientific behavior relate that

the constructivist interpretation considers the products of science as firstand foremost the result of a process of (reflexive) fabrication. Accord-ingly, the study of scientific knowledge is primarily seen to involve aninvestigation of how scientific objects are produced in the laboratoryinstead of a study of how facts are preserved in scientific statements aboutnature. (p. 19)

Furthermore, Latour (1983) believes that it is impossible to do an ethno-graphic analysis of "laboratory life" without perceiving wider social rami-fications:

No matter how divided they are on the sociology of science, the

macroanalysts and the microanalysts share one prejudice: that science

stops or begins at the laboratory walls [sic]. The laboratory is a much

trickier object than that, it is a more efficient transformer of forces than

that. That is why by remaining faithful to his method, the microanalyst

will end up tackling macro-issues as well, exactly like the scientist

[Pasteur] doing lab experiments on microbes who ends up modifying

many details of the whole of French society. (pp. 168-169)

Therefore, I believe that the lure of "pure" science to students who wish

to divest themselves of the messiness of issue-oriented science can only be

re-visioned by engaging them in a science education which resolutely

refuses to clearly demarcate between micro- and macro-levels of "laboratory

life."

Opening "Normal" Science to Constructivist TeachingI believe that biology students would benefit greatly from alternative

pedagogical environments which encourage and enable them to perceivethemselves as engaged actors in science. The situation described above ofone pedagogy for issues in biology and another distinctly different (decid-edly more conventional) pedagogy for "normal" science could easily bechanged to a constructivist approach to both subject areas. It is important

152 VOLUME 3, NUMBER 2

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CONSTRUCTIVISM, COMPUTERS, AND COLLABORATIVE LEARNING

that students construct knowledge in scientific domains with the samecommitments of prior experience and belief that they bring to issues theyimmediately recognize as socially relevant. Therefore, this article is addressedprecisely to both general biology educators and science teaching educatorswho are personally committed to a constructivist perspective in theirteaching and who are also seeking alternative approaches to normativescience curriculum.

Elsewhere we (Jungck, 1982, 1985; Jungck & Calley, 1985, 1986;Peterson, Jungck, Sharpe, & Finzer, 1987; Streibel, Stewart, Koedinger,Collins, & Jungck, 1987; Jungck, Peterson, & Calley, in press) havedescribed how a design approach to learning in science, which is facilitatedby computerized exploratory environments, can be used to enable groupsof biology students to participate knowingly in the social construction oftheir academic scientific knowledge. We have articulated a philosophy ofscience education and a pedagogy (Jungck & Calley, 1986) which con-ceptualizes scientific inquiry as problem posing, problem solving, andpersuasion.

I disagree on two grounds with von Glaserfeld's (1989) assertion that "bysupplying a theoretical foundation that seems compatible with what hasworked in the past, constructivism may provide the thousands of lessintuitive educators an accessible way to improve their methods of instruction"(p. 138). First, I do not know what he gains by so judging intuition. Second,I believe that instantiation of constructivist philosophy is as critical to theadoption of constructivist pedagogy as articulating the philosophy itself.Therefore, 1 present below an instantiation of constructivist pedagogy basedon problem posing, problem solving, and persuasion which has been widelyadopted and which is easily employed with a variety of students in a varietyof contexts.

Problem Posing. Briefly, problem posing is one of the most ideologicallyexplosive activities of science because of the ease of incorporating valueswhich reflect gender, race, ethnicity, and class distinctions (Jungck, 1985).Therefore, 1 believe it is critical, even at the undergraduate level, thatstudents gain considerable experience by posing their own problems (nomatter how long one stands in the lab or the field, problems do not comeautomatically) and carefully articulating their assumptions (including thoseladen with the values of dominant scientific culture).

Typical laboratory experiences are usually inadequate; instructors chooselabs that "work well," represent one protocol of an experiment in the contextof a multitude of experiments, repeat or demonstrate some classical prin-

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ciple that the students already feel they and the instructor understand, andusually focus on the data-collection phase rather than dealing with anyquestion as to problem posing or continuing to analysis, inference, andgeneralization. The widespread availability of tools such as word processors,information banks of data on socially and scientifically important problems,real-time data acquisition systems for collecting and recording raw data, database management systems and spreadsheets for organizing data, statisticalas well as modelling software for analyzing data, and graphics packages forvisually displaying and rotating data can alter this situation. Using suchtools, students can quickly analyze enormous amounts of real data and thusdeal with issues of complexity. Collaborative work is extremely helpfulduring problem posing because reaching a consensual articulation of aproblem frequently elicits such comments as "You can't be sure of that!""Why would you think that?" "But what if that weren't the case?"

Problem Solving. Problem solving often has been discussed within morethan one cognitive psychology approach to science education as an algorithmicprocess or a series of domain-specific heuristics. Biases of problem solvingin biology frequently include problem decomposition which assumes thatreductionism will work (Wimsatt, 1980, 1987). Reductionistic approachesmay ignore ecological interactions or use teleological analyses. Such ap-proaches assume goals of function determine structure instead of Darwinianselection for function from structures which already exist, and parsimonyrather than biological diversity and complexity. Bob Eisenberg, at RushUniversity, has stated, "In biology, Occam's razor cutsyour throat" (personalcommunication, 1990). If we return to our beginning list of issues and howthey oriented problem solving, then environmentally conscious studentsview the analysis of such problems with single tools (for example, a chemicalanalysis of pollutants without concomitantly analyzing the social andindustrial factors contributing to their production) as intellectually andmorallybankrupt.DorothyBuerk(1982)andothers(Rose,1983;Fee,1981;Haraway, 1989; Keller, 1983) have stated that they believe that thesereductionistic, exclusive strategies are some forms of problem solving whichparticularly alienate women. For example, Buerk describes a standard SATquantitative thinking problem: If two people could paint a house in fourdays, how long would it take four people? Her women students did notunderstand why it was wrong to ask if they were painters, whether theybelonged to a union, what the weather conditions were, if they were paid bythe hour or by the job, etc.


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Collaborative work is extremely helpful in problem solving becausereaching a consensual process frequently stimulates more complexity andquestions such as "What should we do next?" "Why would you want to dothat?" "Slow down and let's think about what we did." "But what if thatweren't the case?" In their problem solving groups, students often formdifferent coalitions, change leadership roles, share time in using the com-puter, record data differently, feel a sense of ownership of their problems,and use a variety of intuitive, quantitative, and aesthetic tools for meta-analysis of data. In my experience, the reaction of a group to iterativehypothesis formation—an activity dependent upon data previously collectedwhich contain surprises or which strongly conflict with a priori beliefs—canbe unsettling. This may be because prior assumptions of "scientific method"usually were based upon having one great idea after reducing a large bodyof data collected in a person-free context! However, I believe that only multi-disciplinary, multi-level, and comprehensive problem solving approacheswill have the potential for eventually convincing a community of scholarsas well as the public.

Persuasion of Peers. Results, procedures, and conclusions of research arenot part of science until one persuades members of a research community(peers) that one's hypothesis is a warranted inference from one's experi-ments and/or observations. An acceptable hypothesis should be robust,novel, and significant, thereby having both explanatory power in relating topast scientific knowledge and heuristic value in the design of new experi-ments or the search for new observations. Even if you have performed theneatest experiment in the history of science or observed the most unusualphenomenon, you have not done science until you have persuaded yourpeers. This means that science must be social at this phase no matter howisolated the prior activities of problem posing and solving have been.

Persuasion allows students to argue that the world works in a way thatthey have conceived it. Persuasion establishes their priority, reinforcesgroup commitment, and requires them to learn about the social aspects ofscientific justification. We use peer reviewed "journals" and research postersessions as two mechanisms for disseminating the products of persuasion ina critical and shared context.

Computer ExploratoriumsI coin the concept of computer exploratorium here to make the analogy

with Oppenheimer's famous "Exploratorium" (Hein, 1990) in San Fran-

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cisco, where visitors learn through active participation rather than bypassive museum viewing. Computer exploratoriums represent one environ-ment in which biology students can construct their own academic scientificknowledge via problem posing, problem solving, and persuasion. Toillustrate this approach, I draw my examples from BioQUEST, a nationalconsortium of over 30 curriculum innovators, biology educators, softwaredevelopers, cognitive psychologists, science education researchers, andphilosophers of science who adhere to the "three P's" approach in theirdesign, development, and use of computer exploratoriums. 52 colleges anduniversities around the United States are currently testing the BioQUESTcollection (Jungck, Peterson & Calley, in press). Of its 17 chapters, 12 areassociated with original Macintosh® simulations in genetics, ecology,physiology, biochemistry, and evolution; the other five address the use ofword processing, spreadsheets, statistical packages, modelling packages,and BioQUEST's "labbook." The critical pedagogical features of thesecomputer exploratoriums include allowing teachers and students to workcollaboratively on a problem identified by the students, a problem which hasno single "right" answer and which can be approached through an infinitenumber of experiments.

Problem Posing. Problem posing is fundamental to BioQUEST simula-tions, which have no pre-formed problems or "exercises" with singleanswers and/or algorithmic procedures for stepping through them. Studentsmust be able to develop and iteratively change posed problems. Theyaccomplish this by construction kits (Jungck & Calley, 1986)—a term thatwe use to describe devices which allow a user to build a rich variety ofproblem spaces through the selection of biological phenomena, organismalconstraints, and laboratory or field procedures. For example, what comes upon the initial computer screen of the Genetics Construction Kit is a fieldcollection of fruit flies and a variety of tools for crossing these flies, forsummarizing the results of crosses numerically or histographically, and forperforming statistical tests (see Figure 1).

Similarly, in the Microbial Genetics Construction Kit, the initial screencontains a field collection of colonies on a Petri plate and a selection of toolsfor carrying out replica plating, serial dilution and enumeration, comple-mentation tests, conjugation, and templates for recording inferences (seeFigure 2).

In both instances, the "construction kits" can regulate the kinds ofgenetic phenomena which are operative (dominance, sex-linkage, autoso-mal linkage, multiple allelism, etc.) as well as numerical controls (the


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FIGURE 1

A field collection of fruit flies in the initial screen of the Genetics Construc-tion Kit. If a student chooses a male and female, then a cross can beperformed by selection from the currently grayed out menu bar.

Field Population

FIGURE 2A.

The Start-up screen of the Microbial Genetics Construction Kit.

Uersion 1.B0.4Please choose a problem:

Serial Dilution TourPhenotype Identification TourComplementation TourConjugation TourFull Menus

Cancel Start Problem

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FIGURE 2B

A field collection of bacterial colonies on a Petri plate is the initial screen inthe Microbial Genetics Construction Kit if phenotype identification,complementation, or conjugation problem types are chosen. The studentmust decide what she or he wants to do; there are no word problems or hints,only a lab bench with various tools available.

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number of traits, loci, and alleles as well as the probability of occurrence ofphenomena—see Figure 3). In other words, instead of "hiding" the assump-tions used in a simulation, the computerized "construction kits" explicateand illuminate them for examination and manipulation.

Problem Posing in these environments is compulsory because there areno word problems stated for students. Therefore, they must decide what arewell-framed questions in this discipline. Once a problem has been identified,additional questions have to be addressed: Is the problem soluble with thetools and time available? Will we be able to determine whether the problemhas been "solved?" When? How? Is it a problem or an exercise? Is it asignificant problem in this discipline? Does our statement of the problemlend itself to being iteratively defined with greater precision throughresearch? Has the problem been posed without a teleological, speciesist,racist, sexist, or ethnocentric bias? Group work encourages student mem-bers to define and redefine problems not only initially, but also as they goabout solving "the" problem of their choosing. If the students have not askedthese questions implicitly, then they can be used as explicit reflexivequestions by a teacher who is serving as a collaborator.


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FIGURE 3.

This is one of three screens in the Genetics Construction Kit's "EditProblems" section. Usually these sections are invisible while students areactually working on a problem; I purposely never record these constraintsso I do not know while helping them. (Let me note that I usually learn howto use most of these tools from my students and that I in turn teach themmany of the subtleties that they can be used for.)

| Codominance Probability: ||25 |

I Multiple fllleles Probability:) 50 | Minfllleles: [5~|

MaxRIIeles: |4 |MinUariations: |4 | MaHUariations:|6 ]

03 Linkage MinDistance: 15 MaHDistance:

Probability of Chromosome Switch: 15

| Sen Linkage MinSenLink: [1 I MatiSeHLink 11

( Check ) [ OK j

Problem Solving. Each BioQUEST computer exploratorium has a varietyof tools available on the desk top (a Macintosh® referent to the screenlayout) and each "construction kit" provides specialized sets of tools foranalyzing data. Help is available to the user upon request; however, thesecomments are limited to descriptions of how to use a tool or explanationsof what certain operations do. Since the problem is of the group's choosingand since there are no single correct answers, "help" has nothing to do withproviding answers to a problem.

Teacher collaborators can easily employ problem solving approacheswhich include problem decomposition, the use of qualitative versusquantitative techniques, broad versus deep searches (exhaustive searchesare possible though often not efficient), algorithms-and heuristics, andcomparing and contrasting of iterative experiments. Students rapidly learnthat searching for "the" one correct and proper scientific method which theypresume must exist and most readily applies is usually not a good problemsolving strategy. We refer to BioQUEST simulations as strategic simulations

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TEACHTNG EDUCATION 1991

(Jungck, 1982; Jungck & Calley, 1985, 1986) because they require studentsto develop strategies which plan an immense number of experiments andwhere inference is developed over time rather than as the result of an "aha"following a single great experiment. Thus, problem solving is demystified asa process of genius-like insight and perceived more as a human skill acquiredwith effort and time.

Heuristic problem solving frequently is more a process of being sure thata particular procedure is unlikely to yield useable data than knowing that theprocedure is the best one to employ. A good example of this comes fromprotein sequencing. Protein sequencing is a simple example of problemsolving because most investigators agree that a single, well substantiatedsequence is the desirable goal of a multitude of individual experiments. If apurified protein is hydrolized into amino acids, an investigator may want touse cyanogen bromide to cut the peptide because it cleaves so specifically,after methionine residues. However, if there is no methionine in the proteinor if there are numerous methionines, cutting with cyanogen bromidewould yield too many small peptides to individually sequence and align withother digested fragments. Thus, while an investigator plays hunches basedupon experience, much of the decision making in problem solving entailseliminating alternative solution paths and proceeding down fruitful av-enues. In more socially controversial domains, the cultural contingencies ofproblem solving are usually more transparent to a group of students than innormative science.

Again, a teacher collaborator could use a variety of questions to encour-age students to be more reflexive about their problem solving process. Theseinclude highly specific questions relevant to the computer exploratorium athand, such as: Why do you use the tools (procedures on menu bar) that youdo? Why do you change hypotheses iteratively on the fly? Should sequentialtool use be coordinated? Other questions are more general and could beasked within almost any research investigation. These questions are meantto be more reflexive at a slightly different meta-level. Some examples areWhat algorithms and heuristics are available in this discipline? Have youused problem decomposition and/or qualitative problem redescription?When is computation/modelling appropriate? How would one proceed?What are the advantages and disadvantages of reductionistic researchstrategies in this discipline? Have the following issues been addressed:Efficiency, Robustness, Anomaly resolution, Confirmation, Bias, and Theory-laden observation? Finally, various questions of closure easily arise in theopen ended problem solving of computer exploratoriums: What are your


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criteria for closure; that is, how do you know that you are done? With aparticular tool? With a whole problem space? It is important for a teachercollaborator working towards constructivist learning to avoid "pilot learn-ing;" that is, try to ask strategic questions about why students are doing whatthey are doing or what they will do next instead of getting caught up in thelocal tactics of step-by-step operations. Computer exploratpriums areextremely effective where teachers are working in groups in BioQUESTworkshops and in helping teachers appreciate the importance of usingreflexive questions to facilitate constructivist learning.

Persuasion of Peers. Each BioQUEST computer exploratorium has avariety of tools available on the Macintosh® desk top to encourage andsupport writing and graphic communication. Copy, paste, and clipboardoperations are available, as are the abilities to make notes about possiblehypotheses (our "notepads"), calculations, graphs, or histograms while asimulation is running. Our goal is to help students prepare aesthetic as wellas meaningful reports which they build from the data that they havecollected and to allow them considerable freedom of expression in persuad-ing their peers. Aesthetics are important to student groups because theyrepresent part of the group's ownership of and commitment to theirhypotheses. Cyril Stanley Smith (1975), a metallurgist, goes so far as to assertthat "discovery derives from aesthetically-motivated curiosity and is rarelya result of practical purposefulness" (p. 9).

Since no single right "answer" is available to students or teachers, Iencourage active voice and the use of first person singular or plural. Studentsquickly recognize phrases like "data suggest," "research indicates," and"results show" as rhetorical power-moves on behalf of some authors, usedto intimidate their readers into believing that their assumptions or infer-ences are "intuitively obvious" and value-free. In this way, students need notperceive scientific persuasion as abnormally distinct from other experienceswhere they have had to articulate evidence, take stances consonant withexperience, and be aware of the values of the audience whom they are tryingto convince. Bazerman (1988) and Myers (1990) have written two particularlygood books which focus directly on the larger social process of producinga scientific article and writing grants which lead to them. Furthermore, inclassrooms, writing shifts the arena away from grading for "correct" answers(Stewart & Dale, 1981; Lampert, 1990) to peer review of whether solutionsare warranted, justified, robust, and/or eleganthypotheses. Students there byparticipate in the process where professional evaluation occurs; namely,acceptance into the primary literature involving a process of peer review.

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Once this way of thinking takes root, it changes the teacher's view of"problems" and their solution. No longer would it be possible to cling tothe notion that a given task has one solution and one way of arriving atit. The teacher would come to realize that students may view differentlywhat he or she presents as a "problem." Consequently, the student mayproduce a sensible solution that makes no sense to the teacher. Tellinga student it is wrong is unhelpful and inhibiting (even if the "right" wayis explained), because it disregards the student's effort. Indeed, suchbleak corrections are bound to diminish the student's motivations infuture attempts. In contrast, constructivist teachers would tend toexplore how students see the problem and why their path to a solutionseems promising to them. (von Glaserfeld, 1989, pp. 136-137)The only obvious difference between von Glaserfeld's and BioQUEST's

approach is that we begin by having students pose the problem in the firstplace instead of having them interpret the teacher's representation of aproblem.

Reflective QuestioningThus, after fifteen years of using such computer exploratoriums in

classrooms and workshops for educators, I believe that it is possible to shareseveral very general kinds of questions which one might ask students whilementoring groups who are using computer exploratoriums. As before, thesequestions are organized around the issues of problem posing, problemsolving, and persuasion and are generic rather than specific to a particularproblem in biology.

Because many biology students often consider writing unimportant andbecause they have less experience in scientific discourse, I believe that it isnecessary to have even more reflexive questions about persuasion thanabout problem posing and problem solving. At the most "normative" level,these questions include: Can you integrate/synthesize/consolidate yourknowledge? What have you learned? Can you generalize? (This is importantbecause one goal of the experience is transference.) What patterns did youobserve in your data? How did you persuade yourself? (That is, how did youproduce your results? Did you make predictions?) What is the search spacethat you have explored? (Did you perform an exhaustive search?) What isyour causal reasoning? Have you done "modelling"? What is the role ofdiagrams in presenting ideas? (Note that even a histogram involves suchdecisions as choosing the size of each interval of lumped data.) What use didyou make of Tradition/Literature? What are the conventions within (as well


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as templates of) this discipline? With the scientific community's emphasison priority, what does it mean to be first when you must tie all your effortsto scientific tradition in order to be understood?

While almost all the questions above might be comfortable to a logicalpositivist, a constructivist would want to go beyond those "internalist"questions to ask: What social values were important to the members of yourgroup? Were elegance, beauty and robustness important? What is yourperceptual style? What was significant? (Have you differentiated statisticaltesting versus real world significance?) What voice did you choose and why?Was eloquence relevant? Did you use first person (rather than "datasuggests") ? Did you consider establishing priority and/or the politics of yourposition? Did you consider within science as well as extra-scientific contexts?

When it comes to persuading peers as to whether your group's inter-pretations are significant, robust, warranted, parsimonious, and/or justaesthetic, each group of investigators must deal with a new, but common,set of questions (although explanatory versus experimental sciences,synchronic versus diachronic disciplines, or physical versus biologicalsciences may heartily disagree to their relevance): What defines a singleexperiment? If you change a parameter, is the experiment over when youreturn to a prior state? Is returning to a previously set value a reversibleprocess? (Evolutionarily, did marine mammals become "fish"?) What arethe political interests of authors who impute motives or human character-istics to data? ("The data suggest that," "research tells us that," "ourexperiments indicate," and "it is intuitively obvious" are common ex-amples.) Do even non-invasive measurements interact and change impor-tant biological relationships? How do you define a control? Can you do justone thing?

Inevitably, the one question that students struggle with the most is when areexperiments done? A related question is how do you know that you have doneenough? To clearly distinguish this aspect of persuasion from a similarquestion in problem solving, one could ask the group about the "one-pass"phenomenon:

Thomas Nickles argues that most existing philosophies of sciencecommit the "one-pass fallacy." This is a failure to recognize the extent towhich scientists' own narratives are reconstructions rather than accountsof a single, linear "pass" or sequence of operations. By reconstructionNickles (1988, p. 34) means not the reworking of an argument toincrease its persuasiveness but something that scientists "must do,consciously or not, in order to apply old results and techniques to new

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problems at the frontier and to model one problem solution ontoanother." (Gooding, 1989, p. 426)

Further questions include: Are you done when you run out of ideas? Grantmoney? Time? What are your criteria for closure? What is a solution (qua:an answer)? Are solutions to problems simply hypotheses? When youworked with multiple working hypotheses, how did you know whether onewas better than another?

James van Allen, for whom the earth's radiation belt is named, frequentlyaddresses the Iowa Writer's Project students with the dictum: "Write so thatyou cannot be misunderstood." However, when do scientists purposelyconfuse—conceal important techniques from—or lead astray other scien-tists? This raises a series of related questions: Are clarity and informationdensity the primary characteristics of scientific reports? What are the effectsofanthropomorphic, teleological, racist, sexist, ethnocentric, and/ornation-alistic dominance and language in science? Does secrecy have value in ademocratic scientific community? If so, when? What if your ideas are toorevolutionary? How do you personally cope with "the problem of balancingresponsible investigative research with effective advocacy"? (Simon, 1986,p. 172).

Persuasion in science is not strictly limited to scientific articles in primaryresearch journals. Thus, we might ask what non print media lead topersuading peers? What is the role of scientific meetings? What are the risksof releasing "results" to TV and other "news" organizations before publish-ing in refereed journals?

Obviously, the list of questions could continue. However, I have usedsuch questions with much success and have found that many of the abovequestions raised over and over again help students 1.) to identify implicitassumptions and make them explicit, 2.) to learn that making mistakes is apowerful tool involved in learning to "debug," edit, and to be self-critical andself-correcting, 3.) to learn that you haven't done science unless you haveconvinced your peers that your hypothesis is significant by a long series ofcriteria which are not universally self-evident, 4.) to learn that you cannotlearn social values in science unless you work collaboratively to unferret oneanother's assumptions (The famous anthropological dictum applies: "Don'texpect a fish to discover water."), and 5.) to learn there is no fundamentaldifference between relevant social problem posing, problem solving, andpersuasion and that which occurs in "normal" science.


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Collaborative Learning and CurriculumBoth high school and college biology educators who have participated in

BioQUEST workshops immediately recognized three challenges of employingsuch computer exploratoriums in their classrooms. First, some consider ita risk to teach without knowing "the answers." Second, their role changesby working with problems that the students themselves pose, with data thatthe students have collected, and with inferences developed by the students.Third, since the students usually are working in groups of three, much of theteaching, by necessity, involves mediating group processes and enhancingintragroup dynamics rather than focussing on subject material. Furthermore,in workshop wrap-ups, participants are vocal about how they themselveswere learning and restructuring their knowledge. Because the computerexploratoriums allow students to rapidly illustrate their problem-solvingapproach to one another and use various kinds of "thinking aloud" strategiesto get at their own beliefs and to understand others', these teachers oftenvoice that they are consolidating their knowledge more personally than ithas been parsed by textbook chapters in their own education. Von Glaserfeld(1989) summarizes this more formally:

In this context, it is necessary to emphasize that the most frequent sourceof perturbations for developing cognitive subject is the interaction withothers. This, indeed, is the reason constructivist teachers of science andmathematics have been promoting "group learning," a practice that letstwo or three students discuss approaches to a given problem, with littleor no interference from the teacher. (p. 136)Furthermore, the sharing of knowledge about one another's algorithms

and heuristics seems easier to these teachers and students because of thereadily demonstrable nature of computer simulations. BioQUEST membersdo not believe that one teaching method or role is best. We see an open andquestioning approach within environments such as investigative labs orcomputer exploratoriums as pivotal components of active learning.

The creative tension in teaching science collaboratively where noanswers exist a priori is consonant with teachers' prior experience in doingscientific research. Yet it is important to associate such an open-endedteaching process with examples of a dialectical process that exist in otherdomains within their own schools. For example, the creative writing teacherfrequently has students initiate their own ideas for a composition and asksstudents what it is that they would like to express, what persuasive form(novel, short story, play, poem) they would like to use, and why that formmight be best for their purposes. Kenneth Brufee (1983) notes:

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that research is not complete until it has been reported; that is, until it hasbeen offered to a community of knowledgeable peers for response. Thus,writing groups are really rewriting groups, in which students find out, manyfor the first time, that rewriting is not punishment for doing badly the firsttime but an opportunity to offer one's best work to the community that counts.(p. 28) (author's emphasis)Similarities to scientific research further abound; for example, Larry

Schwartz states that "to learn to debug is to learn to overcome the fear ofbeing wrong, replacing it with the drive to untangle the problem" (1983, p.33). In this case, a writing teacher is using the normative process of computerprogramming to describe novice writers learning that 90% of the process ofwriting is editing, not composition. Marvin Minsky describes this asdeveloping "a positive attitude towards making errors" (1986, p. 97).Schwartz also shows that re-visioning creates more personal commitmentamong students because writing is reconceptualized as an expression oftheir attitudes, experiences, and rationality which helps to create "a trulycollaborative relationship with the writing teacher" (p. 34).

Such comparisons of science with the arts of writing, sculpting, painting,composing music, and so forth illustrate the potential for a "third culture,"such as described by C. P. Snow, occurring through the creation of agenuinely transformative curriculum. Doll (1988) states the values of thisshift:

A transformative curriculum focuses on the qualitative changes theparticipants—teachers as well as students—go through as they engage inthe curriculum: here curriculum is considered as a process of engagementnot as a "course to be run." (p. 127)The use of computer exploratoriums enhances the potential for science

educators to build such transformative curricula; BioQUEST in biology andPriscilla Laws' Workshop Physics (1990), a series of real-time data acquisitionlabs in physics, are simply two instantiations of such praxis.

The role of collaborative learning in these curricula is different from thatespoused in those texts on group work, where the focus is on assimilatinginformation. Students can gain considerable insight about open-ended,collaborative problem solving by considering some techniques employed incollaborative learning—like "fermenting":

Fermenting requires the skills needed to stimulate reconceptualizationof the material being studied, cognitive conflict, the search for moreinformation, and the communication of the rationale behind one'sconclusions. Some of the most important aspects of learning take place


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when group members skillfully challenge each other's conclusions andreasoning. Academic controversies cause group members to dig deeperinto the material, to assemble a rationale for their conclusions, to thinkmore divergently about the issue, to find more information to supporttheir positions, and to argue constructively about alternative solutions ordecisions. (Johnson, Johnson, Johnson-Holubec, & Roy, 1984, p. 47)

About another example,"The Group Investigative Method" of Sharan andHertz-Lazarowitz, Cohen (1986) states:

Group investigation is intellectually ambitious. Students play the role ofcreative research scholars. In order to achieve these goals, they mustwork together closely. Good group process is insured in various ways:building commitment to the group and its project, use of division oflabor, and group process skills. (p. 90)In sum, a variety of role models for effective investigative collaborative

learning do exist—from the elementary classroom studies by Sharan andSharan (1990) to the scientific research communities studied by Latour andWoolgar (1986). The challenge to constructivist biology educators is toreconceptualize our learning environments to enhance these collaborativeprocesses of constructing knowledge.

ConclusionI believe that input from a wide community of constructivist scholars and

teachers will profoundly improve biology education by developing futurebiologists and biology teachers who have a much better understanding ofscientific investigation through their own development and use of investigativesoftware, laboratory, and field activities. This community of teachers andscholars should have biologists of many varieties, researchers in scienceeducation and educational technology, computer scientists, and philoso-phers of science. Based on my commitment to transforming the nature andquality of science education, I believe that exploratory environments onmicrocomputers will empower many co-learners (teachers and students)primarily by conflating these two previously polar roles. Computerexploratoriums are not a panacea (in particular, this approach alone can dolittle to change who per se it is that does science), but they can provide anenvironment in which students can have ample opportunity to develop theirconfidence and competence in problem posing, long-term inference mak-ing, and contextualized problem solving through experiential and col-laborative learning. co

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Note1. I sincerely thank those colleagues who have so greatly contributed to the

development of the ideas presented herein: Susan Jungck, John N. Calley, AngeloCollins, Chuck Dyke, Robin Greenler, Nils Peterson, Frank Price, Scott Roberts,John Rosenwald, Patti Soderberg, Jim Stewart, and Bill Wimsatt. They haveendured numerous discussions and helped me to articulate, defend, and developa practice which has been more collectively grown than individually designed.Dan Marshall, Teresa Holevas, Susan Jungck, Angelo Collins, and Jenn Tiptonmade close readings of earlier drafts, and their editorial comments have substantiallyimproved the manuscript. To these colleagues and all the members of BioQUEST,I am sincerely grateful.

ReferencesBazerman, C. (1988). Shaping written knowledge: Essays in the growth, form, function,

and implications of the scientific article. Madison: University of Wisconsin Press.

Brannigan, A. (1979). The law valid for Pisum and the reification of Mendel. In A.Brannigan, The social basis of scientific discoveries (pp. 89-119). Cambridge, MA:Cambridge University Press.

Brufee, K. (1983). Teaching writing through collaboration. In C. Bouton & R. Garth(Eds.), Learning in groups: New directions for teaching and learning (pp. 23-29). SanFrancisco, CA: Jossey-Bass.

Buerk, D. (1982). An experience with some able women who avoid mathematics. Forthe Learning of Mathematics, 3(2), 19-24.

Cohen, E. (1986). Designing groupwork: Strategies for the heterogeneous classroom.New York: Teachers College Press.

Collins, A. (1986). Strategic knowledge required for desired performance in solvingtransmission genetics problems. Dissertation Abstracts International, 47, 1269A.(University Microfilms No. 86-11, 405)

Doll, W. (1988). Curriculum beyond stability: Schön, Progogine, Piaget. In W. Pinar(Ed.), Contemporary curriculum discourses (pp. 114-133). Scottsdale, AZ: GorsuchScarisbrick.

Fee, E. (1981). Is feminism a threat to scientific objectivity? The Journal of CollegeScience Teaching, 11(2), 84-92.

Fleck, L. (1979). Genesis and development of a scientific fact. Chicago, IL: Universityof Chicago Press. [Originally published in 1935.]

von Glaserfeld, E. (1989). Cognition, construction of knowledge, and teaching.Synthese, 80(1), 121-140.

Gooding, D. (1989). How to be a good Empiricist. The British Journal for the Historyof Science, 22(75), 419-427.

Haraway, D. (1989). Primate visions: Gender, race, and nature in the world of modernscience. New York: Routledge.

Hein, H. (1990). The exploratorium: The museum as laboratory. Washington, DC:Smithsonian Institution Press.


Dow

nloa

ded

by [

New

Yor

k U

nive

rsity

] at

16:

08 1

0 O

ctob

er 2

014


Johnson, D.Johnson, R., Johnson-Holubec, E., & Roy, P. (1984). Circles of learning:Cooperation in the classroom. Alexandria, VA: Association for Supervision andCurriculum Development.

Jungck, J. (1982). Strategic simulations: Experiencing research-like problem solvingbehavior in a computer biology module. In L. Meeth & D. Gregory (Eds.),Directory of teaching innoxations in biology (pp. 133-134). Dunedin, FL: Studies inHigher Education.

Jungck, J. (1985). A problem posing approach to biology education. American BiologyTeacher, 47(5), 264-266.

Jungck, J., & Calley, J. (1985). Strategic simulations and post-Socratic pedagogy:Constructing computer software to develop long-term inference through ex-perimental inquiry. American Biology Teacher, 47(1), 11-15.

Jungck, J., & Calley, J. (1986). Genetics: Strategic simulations in Mendelian genetics.Wentworth, NH: COMPress.

Jungck, J., Peterson, N., & Calley, J. (Eds.). (in press). BioQUEST: Quality under-graduate educational simulations and tools in biology. Reading, MA: Addison-Wesley.

Keller, E. (1983). A feeling for the organism: The life and work of Barbara McClintock.New York: Freeman.

Knorr-Cetina, K. (1983). The ethnographic study of scientific work: Towards aconstructivist interpretation of science. In K. Knorr-Cetina & M. Mulkay (Eds.),Science observed: Perspectives on the social study of science (pp. 115-140). BeverlyHills, CA: Sage.

Lampert, M. (1990). When the problem is not the question and the solution is not theanswer: Mathematical knowing and teaching. American Educational Research

Journal, 27(1 ), 29-63.?

Latour, B. (1983). Give me a laboratory and I will raise the world. In K. Knorr-Cetina& M. Mulkay (Eds.), Science observed: Perspectives on the social study of science (pp.141-170). Beverly Hills, CA: SAGE.

Latour, B., & Woolgar, S. (1986). Laboratory life: The social construction of scientificfacts. Princeton, NJ: Princeton University Press.

Laws, P. (1990). Workshop physics: The calculus-based activity guide. Portland, OR:

Vernier Software.

Marcus, G. (1990, May 18). Review of Primate Visions. Science, 248(49-57), 886-887.?

Minsky, M. (1986). The society of mind. New York: Simon & Schuster.Myers, G. (1990). Writing biology: Texts in the social construction of scientific knowl-

edge. Madison: University of Wisconsin Press.

Nickles, T. (1988). Discovery and experiment. In D. Batens & J.P. van Bendegem(Eds.), Theory and experiment: Recent insights and new perspectives on their relation(pp. 20-40). Dordrecht, Netherlands: Reidel.

Peterson, N., Jungck, J., Sharpe, D., & Finzer, W. (1987). A design approach toscience: Simulated laboratories: Learning via the construction of meaning.Machine-Mediated Learning 2(1/2), 111-127.

169

Dow

nloa

ded

by [

New

Yor

k U

nive

rsity

] at

16:

08 1

0 O

ctob

er 2

014


Rose, H. (1983). Hand, brain, and heart: A feminist epistemology for the naturalsciences. Signs: Journal of Women in Culture and Society, 9(11), 73-90.

Schwartz, L. (1983, June). Teaching writing in the age of the word processor andpersonal computers. Educational Technology, 33-35.

Sharan, Y., & Sharan, S. (1990). Group investigation expands cooperative learning.Academic Leadership 47(4), 17-21.

Simon,H. (1986). Persuasion: Understanding, practice, and analysis, 2nd ed. New York:Random House.

Smith, C. (1975). Structural hierarchy in science, art, and history. In J. Weschsler(Ed.)., On aesthetics in science (pp. 9-53). Cambridge, MA: MIT Press.

Stewart, J. & Dale, M. (1981). Solutions to genetics problems: Are they the same ascorrect answers? The Australian Science Teacher 27(3), 59-64.

Streibel, M., Stewart, J . , Koedinger, K., Collins, A., & Jungck, J. R. (1987). MENDEL:An intelligent computer tutoring system for genetics problem-solving, conjecturing,and understanding. Machine-Mediated Learning 2(1-2), 129-159.

Wimsatt, W. (1980). Reductionistic research strategies and their biases in the unitsof selection controversy. In T. Nickles (Ed.), Scientific discovery - Volume II: Casestudies (pp. 213-259). Dordrecht, Netherlands: Reidel.

Wimsatt, W. (1987). False models as means to truer theories. In M. Nitecki (Ed.),Neutral models in biology (pp. 23-55). Chicago: University of Chicago Press.


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Documents

Constructivism, Computer exploratoriums, and Collaborative learning: Constructing Scientific Knowledge