restructuring knowledge in biology: cognitive processes and metacognitive reflections

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Restructuring knowledgein Biology: cognitiveprocesses and metacognitivereflectionsBarbara L. Martin , Joel J. Mintzes & Ileana E.ClavijoPublished online: 20 Jul 2010.

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INT. J. SCI. EDUC., 2000, VOL. 22, NO. 3, 303- 323

Restructuring knowledge in Biology: cognitiveprocesses and metacognitive reflections

Barbara L. Martin, Joel J. Mintzes, and Ileana E. Clavijo, Department ofBiological Sciences, University of North Carolina at Wilmington, USA.

This follow-up study explored successive and progressive changes in the structural complexity andpropositional validity of knowledge held by students enrolled in an advanced, undergraduate, univer-sity-level biology course. To examine the way learners restructure their knowledge during the semester,subjects constructed concept maps at intervals of four-six weeks. The concept maps were scored forstructural complexity and propositional validity based on a modified version of Novak and Gowin’smethods, and for structural change based on the work of Rumelhart and Norman.

Results support previous findings that a significant amount of ‘weak’ knowledge restructuring occurs,resulting in an incremental, cumulative and limited form of conceptual change. However, the most‘radical’ type of ‘strong’ restructuring (involving wholesale, abrupt and extensive change) is concen-trated during the first six weeks of the course. The findings also suggest that gender plays a significantmediating effect in knowledge restructuring. Significant differences favouring females were found in thenumber of crosslinks depicted and a significant interaction, also favouring females, was found in theamount of branching.

Finally, clinical interviews with rote and meaningful learners suggest that the latter group displays anenhanced level of awareness, and a stronger ability to monitor, regulate and control their own learning.The significance of these findings for research and practice are discussed.

Introduction

In this paper we report on the third in a series of longitudinal studies focusing onknowledge restructuring and conceptual change in college-level biology. Buildingon work of previous investigators (Pearsall et al. 1997), we examine changes instructural complexity and propositional validity seen in the knowledge frameworksof students enrolled in an upper-level, university biology course over a 16 weekperiod. Using a set of concept mapping exercises and clinical interviews admin-istered at four-six week intervals throughout the semester, the current studyexplores evidence:

. that change in the structural complexity and propositional validity ofstudents’ knowledge structures is extensive and incremental over the courseof a semester;

. that gender plays a significant role in mediating these changes;

. that incremental change in structural complexity and propositional validityis concurrent with periods of ‘weak’ and ‘strong’ restructuring, and

. that differences exist in metacognitive reflections of students employingdiverse learning strategies.

International Journal of Science Education ISSN 0950-0693 print/ISSN 1464-5289 online # 2000 Taylor & Francis Ltdhttp://www.tandf.co.uk/journals/tf/09500693.html

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Recent studies on knowledge restructuring and conceptual change have begun toshed light on the cognitive and metacognitive events that underlie classroom learn-ing in the natural sciences (Novak and Musonda 1991, Songer and Mintzes 1994,Markham et al. 1994, Trowbridge and Wandersee 1994, Pearsall et al. 1997,Griffard and Wandersee 1999). Many studies suggest that good school science pro-duces learning that is gradual and assimilative in nature; it depends on a weak formof knowledge restructuring, and results in an incremental, cumulative and limitedform of conceptual change (Carey 1987, Mintzes et al. 1997, 1998, 2000). This formof learning is consistent with a set of cognitive events that Ausubel et al. refer to assubsumption, wherein new and less-inclusive concepts are linked to a hierarchicalframework of closely-related, more general and inclusive ideas (Ausubel et al. 1978).When this form of learning is successful, substantial integration of new subordinateconcepts is accomplished in a relatively brief period of time.

In addition however, successful science learners also engage in a strong orradical form of knowledge restructuring, resulting in wholesale, abrupt and exten-sive modifications in conceptual understanding. The latter type of science learningis consistent with the cognitive processes that Ausubel et al. refer to as super-ordination. Superordination occurs when new, more general and inclusive con-cepts subsume existing concepts within the learners’ knowledge structure. Incontrast to subsumption learning where integration is relatively rapid, the integra-tion of new superordinate concepts requires a more lengthy ‘latency’ period.

From an epistemological viewpoint, weak restructuring generally results in apiecemeal assimilation of new concepts, and learners typically report experiencinga gradual ‘building up’ of novel ideas as some concepts survive and others aredisplaced (Toulmin 1972). In contrast, strong or radical restructuring and sub-sequent integration is characterized by the ‘flash of insight’ or the ‘ah ha!moments’ that accompany paradigm shifts or major conceptual ‘revolutions’(Kuhn 1962).

Beyond these cognitive issues, it now appears that ‘metacognitive’ or ‘execu-tive control’ factors play a significant role in successful science learning. For ex-ample, recent studies have begun to illuminate differences in the way ‘novices’ and‘experts’ monitor, control and regulate their own learning (Simon and Simon1978, Larkin et al. 1983, Chi et al. 1988). Related studies suggest that ‘successful’and ‘unsuccessful’ science learners solve problems in substantially different ways(Smith 1990). The weight of evidence suggests that science learners who employ‘meaningful’ (Ausubel et al. 1978) learning strategies are more cognizant of theirown learning and better able to regulate and control it than ‘rote’ learners. It maybe that this enhanced metacognitive ability is responsible in part for success inresolving solution paths in complex knowledge domains such as genetics andNewtonian mechanics.

To explore these issues in further depth, this study examines the way learnersenrolled in an advanced university biology course restructure their knowledge overa 16 week period, and the extent to which they accurately reflect on their ownlearning success. Specifically, the study explores the following hypothetical trendsbased on previous work (Pearsall et al. 1997):

. H1: A progressive, incremental, and cumulative shift toward greater struc-tural complexity and propositional validity of knowledge held by studentsover the 16 week period;

304 B. L. MARTIN ET AL.

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. H2: Gender differences in structural complexity and propositional validityof knowledge, favouring females;

. H3: An early, brief period of strong or radical knowledge restructuringfollowed by a more lengthy period of weak restructuring;

. H4: Differences in the validity and accuracy of metacognitive reflections,favouring meaningful learners.

Method

Context

Subjects of this study were 74 third and fourth year students (36 males, 38 females)who enrolled in a ‘300-level’ course in marine biology at a regional campus of acomprehensive level I state university in southeastern North Carolina, USA. Thetotal institutional enrolment is about 10,000 and the mean adjusted SAT scores ofincoming students is 1070, place them slightly above the national mean in verbaland mathematical aptitude. Approximately 80% of all students are state residents.

The course enrols 65-70 students each semester, and is traditional in virtuallyevery respect: students attend three 50-minute lectures and one four hour labora-tory each week. The lecture room accommodates the entire class and the labora-tory has a seating capacity of 24. The instructor (a female with expertise infisheries biology) is an experienced associate professor who has taught the coursefor over 10 years. Laboratories are taught by graduate students enrolled in theM.S. and cooperative Ph.D. programs in Marine Biology. Assessment of studentperformance is also conventional in most ways; grades are based on three ‘mid-terms’, a comprehensive final examination and weekly laboratory quizzes andreports. The overwhelming majority of assessment items are composed of ‘com-prehension’ and ‘application’ level questions.

The course, which offers an introduction to coastal and marine biotic com-munities and abiotic components of these ecosystems, is normally taken in thethird year by all students who enrol in the B.S. degree program in marine biology.Major concepts presented in the course are: chemical and physical properties of seawater; speciation and biogeography; benthic and intertidal communities; rocky,muddy and sandy shorelines; estuaries, marshes, coral reefs; and ecological effectsof human activity. Prerequisites for the course are one semester courses each in thebiology of cells, plants and animals. Most students have taken additional courses ingenetics, ecology and introductory chemistry.

Data for this study were collected over a two semester period, and all analysesare based on the pooled data set. During this time period, the course syllabus, theinstructor, and the planned lecture and laboratory schedules remained unchanged.

Overview: a replication with variation

The procedures employed in this study attempt to replicate, where possible, thosedeveloped in previous work involving first year science and nonscience students(Pearsall et al. 1997). Except for variation introduced by differences in knowledgedomain, course level, and idiosyncrasies of individual students and instructors, wehave attempted to follow the same fundamental research design as previously.Although it is never entirely possible to make direct and accurate comparisons

RESTRUCTURING KNOWLEDGE IN BIOLOGY 305

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of research findings across studies in science education, we hope the findings of thecurrent study will complement earlier work on first year university students, andencourage others to contribute to the effort by designing and implementing similarlongitudinal studies.

Concept maps

In this study the concept mapping procedure (Novak and Gowin 1984) was used toexamine knowledge restructuring at four successive intervals during the semester.A concept map is a two dimensional, hierarchical, node-link representation thatdepicts the major concepts and relationships in the knowledge structure.

Students received oral and written instruction in concept mapping (Arnaudin1985) and a brief opportunity to practise the technique during the first week of thesemester, and were asked to map their understandings of Life in the Ocean. Thecompleted maps were returned to students at 4-6 week intervals throughout thesemester with instructions to ‘draw another map that shows your current under-standing of life in the ocean.’ Students were encouraged to review their previousmap, and either to discard it and begin anew or to redraw it, adding to and chang-ing its content to reflect their current understanding. At the end of the semester, wehad a set of four maps from each student. To enable us to differentiate changesmade to their maps during subsequent time intervals, students received a uniquelycoloured pencil at each administration of the task (Time 1, black; Time 2, red andso forth).

The reliability and validity of concept map scoring techniques have beenreviewed elsewhere (Shavelson and Ruiz-Primo 2000). In the current study twoscoring approaches were employed: a modified version of Novak and Gowin’smethod (Markham et al. 1994) for scoring structural complexity and propositionalvalidity, and a newer method (Pearsall et al. 1997) for scoring structural changebased on the work of Rumelhart and Norman (1978).

The modified Novak/Gowin method assigns points for: concepts (one point foreach nonredundant concept); relationships (one point for each valid, scientifically-acceptable proposition); hierarchy (five points for each level of hierarchy); branch-ing (one point for the first branching and three points for each additional branch-ing); and cross-links (10 points for each valid, scientifically-acceptable cross-link).A new scoring category, interconnectedness (cross-links/concepts £ 100), has beenadded to our scoring regime on a trial basis. Although cross-link scores provide ameasure of ‘integration’ in the knowledge structure, we have found that the ratio ofthe cross-links to concepts scores expressed as a proportion, offers a more mean-ingful indication of the ‘cohesiveness’ of the knowledge framework.

Based on Rumelhart and Norman’s ‘Active Structural Networks’ theory, wedeveloped a second scoring approach which enables us to document the frequen-cies of three structural changes in the knowledge frameworks: restructuring (when aconcept label is added to or deleted from the first hierarchical level of a conceptmap); accretion (when 10 or more concept labels are added to a pre-existing con-cept, resulting in a differentiation or elaboration of a pre-existing concept); andtuning (when any change to a pre-existing concept results in a modification of itsmeaning by the addition of constraining or constant variables). In this scoringapproach, each map is compared to its successor (map 1 vs. map 2; map 2 vs.map 3; and map 3 vs. map 4), and a score of 1 or 0 is recorded for each instance or


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non-instance of a structural change event. Accordingly each student receives ninescores (3 comparisons £ 3 events/comparison). The interjudge reliability of thisscoring approach was found to be 0.96 (Pearsall et al. 1997).

Predominant learning modes

Students were characterized by their predominant learning mode (meaningful vs.rote) based on responses to a set of self-report statements of learning behaviourembedded within the Schmeck et al. (1977) Inventory of Learning Processes (ILP)as in Fisher (1991). Scores on the Synthesis-Analysis and Elaborative Processingsubscales were summed, and the total score was used as the criterion for selectionof interview subjects. The summed scores ‘appear to assess the tendency to take anactive rather than a passive role in processing new information . . . (and) the habi-tual use of ‘‘deep’’ rather than ‘‘shallow’’ information processing strategies’(Schmeck et al. 1977). The development and validation of the ILP, and its usein assigning group membership based on ‘predominant learning mode’ isdescribed elsewhere in depth (Pearsall et al. 1997). In the current study, studentsachieving the five highest scores (meaningful learners) and those achieving the fivelowest scores (rote learners) were selected for a series of indepth clinical interviews.

Clinical interviews

Structured but flexible clinical interviews were designed to encourage students toreflect on and describe their own learning strategies, and to assess and evaluatetheir success in achieving course goals. Thus, the purpose of the interviews was toprobe metacognitive knowledge and if possible to relate that knowledge tostudents’ predominant learning modes.

Interviews were conducted individually within the privacy of an enclosedoffice space and were audiotaped for later transcription and analysis. Ten subjectswere interviewed within a week of completing each of their second, third andfourth concept maps. The basic structure of the interviews varied, as the inter-viewer followed up on potentially important responses, however, every interviewincluded the following items:

[Placing the student’s previous concept map on the table] Tell me what you werethinking about when you drew your last concept map.

How do you feel about the map now? How could you have improved your conceptmap?

How do you think your ideas compared with other students in the class?

[Placing the student’s current concept map on top of the previous map]. Now let’s takea look at the map you just completed. Tell me as much as you can about what youwere thinking as you drew this map.

[Placing the previous and current maps next to each other] Looking at the two mapstogether, tell me how your ideas have changed. How do you feel about this new map?

Tell me how you go about studying for this class.

The interviews ranged in length from 10-45 minutes. The mean interview lengthwas approximately 25 minutes.


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Analysis

Differences in mean total concept mapping scores based on the Novak/Gowinscoring method were analysed as a function of time in a series of one-way analysesof variance. The effect of gender on mean concept mapping scores was explored ina set of 2 (gender) £ 4 (time interval) factorial analyses with repeated measures.The Wilk’s lambda criterion was used to approximate the F statistic. Data derivedfrom the Rumelhart/Norman scoring technique were examined by frequencyanalyses.

A simplified form of the ‘constant comparison’ procedure (Glaser and Strauss1967) was employed in analysing the interview transcripts. Following each set often interviews, the audiotapes were transcribed; the transcripts and concept mapswere reviewed, and preliminary hypotheses were posed. The preliminary hypoth-eses and emerging generalizations served as starting points in subsequent interviewsessions. In this paper we present concept maps and salient interview responses oftwo subjects, Rhonda* (a rote learner) and Michelle* (a meaningful learner)[*pseudonyms].

Results and discussion

Structural complexity and propositional validity

A summary of concept map total scores based on the modified Novak/Gowinmethod is given in figure 1. The ‘structural complexity profiles’ reveal significantdifferences … p < 0:01† in all scoring categories over the course of the semester. Themost substantial growth is seen in the concepts, relationships, and branching cate-


Figure 1. Summary of concept map total scores based on modifedNovak/Gowin scoring method [Means and Standard Deviations].(N ˆ 74).

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gories where scores improved from 200-300% during the 16 week time period.Additionally, the crosslinks scores grew by approximately 100%. While growth inthe hierarchies category was significant (30%), given the minimal size of this growthit is doubtful whether much functional meaning can be attributed to it.

Because crosslinks are intended to reflect the extent of integration in theknowledge framework, and integration is commonly and most closely associatedwith working expertise in scientific knowledge domains (Chi et al. 1988) we soughta way of examining the relative increase in crosslinks as a function of estimatedoverall growth in the knowledge framework. Using the concepts score as a grossestimate of the magnitude of relevant knowledge, we examined change in theproportion of crosslinks to concepts over the 16 week period. The proportion,crosslinks/concepts £ 100, we labelled interconnectedness . The latter score declinedon average by 100% during the course of the semester. The proposed significanceof this decline will be discussed in the conclusions.

Gender effects

The effects of gender and time on concept map scores are summarized in figure 2.The two-way analyses of variance reveal significant main effects due to time in allscoring categories … p < 0:01† and to gender in the crosslinks category … p < 0:05†.Additionally, a significant interaction was found in the branching category… p < 0:01†. In all analyses, the effects attributable to time are consistent withthose found in the previous one-way analyses, suggesting a gradual, stepwiseand cumulative growth in structural complexity and propositional validity. In


Figure 2. Effects of gender and time on concept map scores.

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the cases of differences attributable to gender, the main effects (crosslinks) andinteractions (branching) favour females.

Structural change

Figure 3 summarizes the frequencies of structural changes in the knowledge fra-meworks based on the Rumelhart/Norman scores. The findings suggest that tuningis a type of structural change that characterizes virtually all learners, and its fre-quency remains stable throughout the semester. Accretion is somewhat less com-mon, occurring in approximately 80% of students during the first 12 weeks;dropping to about 50% in the last month. The most dramatic change occurs inthe restructuring component. Approximately 60% of all learners make changes atthe highest level of their knowledge frameworks during the first six weeks of thesemester. This frequency declines to some 30% in the next six weeks, and to lessthan 20% in the final four weeks.

Metacognition

In this final section we compare and contrast two cases (‘Rhonda’ and ‘Michelle’)of students with substantially different learning approaches. Rhonda achieved ascore of nine (out of 32) on the combined subscales of the Inventory of LearningProcesses, placing her among the lowest scoring five students in the class. Sherevealed to the interviewer her trepidation about the concept mapping task, having


Figure 3. Frequencies (%) of structural changes based on operationalizedRumelhart/Norman scoring method.

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taken her last biology course two years ago. Michelle, a confident senior, scored 28on the ILP, placing her among the five highest scoring students.

A longtitudinal overview of Rhonda and Michelle’s concept map scores(table 1) is quite revealing. At the beginning of the semester, Rhonda trailsMichelle in four of the six scoring categories; by the end, she trails in all six.Interestingly, their scores diverge over the course of the semester, and the diver-gence seems to have a somewhat regular slope that might be described as ‘gradualbut steady’, with a kind of fixed trajectory. For example, in the branching categoryRhonda begins the semester with a score of 19 and ends with 61; whereas Michellebegins with 43 and ends with 121. This pattern suggests that the advantagesconferred by meaningful learning are established at an early stage and continueunabated throughout the entire semester. In the end, Rhonda failed the laboratoryportion of the course, but received a passing overall grade of ‘C’. Michelle receivedan ‘A’ in the course, and is currently enrolled in the M.S. graduate program inmarine biology.

Rhonda’s first and second concept maps are shown in figures 4 and 5.Interestingly, the first map is quite complex; most of the propositions are valid,and the amount of crosslinking is extensive. From a qualitative perspective how-ever, the level of sophistication revealed in the concept labels is not very great. Apotentially troublesome feature of the map is her designation of ‘plants’ and ‘ani-mals’ as superordinate concepts. To most contemporary biologists, the absence of‘fungi’, ‘protists’ and ‘monerans’ suggests a limited understanding of the diversityof life forms in the ocean.


Table 1. A summary of Rhonda and Michelle’s concept Map scores

Concept map score by week

Scoring category Week 1 Week 6 Week 12 Week 16

ConceptsRhonda 13 31 59 70Michelle 45 61 105 168

RelationshipsRhonda 12 30 58 70Michelle 43 64 105 169

HierarchiesRhonda 10 20 20 20Michelle 25 25 30 30

BranchingRhonda 19 25 55 61Michelle 43 55 85 121

CrosslinksRhonda 150 190 50 60Michelle 120 50 170 200

InterconnectednessRhonda 1154 613 85 86Michelle 267 82 162 119

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The second map reveals some interesting changes; most striking is the accretion ofmany new concept labels. More importantly however, is a restructuring in whichthe taxonomic designations (‘plants’ and ‘animals’) are replaced with the ecologicaldesignations (‘producers’ and ‘consumers’). Again she omits a large and very sig-nificant (but covert and inaccessible) group of organisms, i.e. ‘decomposers.’ Sincedecomposers are essentially an ecological designation for certain fungi and mon-erans, it seems probable that her limited understanding of organismic diversity hasconstrained her understanding of life in the ocean. Significantly, Rhonda’s secondmap also offers no indication that she has assimilated the significance of ‘abiotic’(non-living) factors in the marine environment.

When Rhonda was asked about her first map, she seemed to understand theimportance of the superordinate concepts in ordering her thinking. There was alsoan indication however that she might be experiencing some significant memoryretrieval problems, and is having some difficulty with the concept mappingprocedure:

I: How could you have improved on this [first] concept map?

R: . . . Put more categories, started it differently, because that’s what I did the first time isstarted it differently, the second time I mean. I don’t know . . . maybe if I sat there a littlebit longer I probably could have thought of more things to do with it. . . . The first time Ididn’t know that much, plus I wasn’t really into it.

Probing further into Rhonda’s approach to learning, it became clear that she wascognizant of her rote learning style and suggested that she used memorization tosave time:

I: Can you tell me a little bit about how you study for this class?

R: For the first test, I basically memorized it. I basically went through my notes andmemorized the key concepts. I thought about doing concept maps and stuff like that, butwith all my other classes and stuff, I haven’t really organized my time yet in order to makeout an actual concept map. So basically I memorized this and then I went with anotherperson and we just asked each other questions. . . .

Michelle’s first concept map (figure 6) is also quite revealing. As with Rhonda, herfirst level superordinate concepts are ‘plants’ and ‘animals.’ However, unlikeRhonda, these labels subsume some fairly sophisticated prior knowledge. For ex-ample, she has included some important marine ecosystems (e.g. open ocean, coralreefs, salt marshes and tidal areas); differentiates between and explains the signif-icance of ‘true plants’ and phytoplankton, and identifies a number of importantvertebrate and invertebrate animal groups at a taxonomic level (e.g. agnatha, tele-osts, chondrichthyes, pinnipeds).

A partial representation of Michelle’s second concept map (figure 7) depicts asignificant restructuring of her knowledge at the highest level. Unlike Rhonda,however, the restructuring Michelle depicts is fully consistent with the knowledgeorganization represented in the textbook and the lectures, and is congruent withthe instructor’s emphasis on the importance of ‘biotic’ and ‘abiotic’ components ofthe marine environment. Unfortunately, there is no evidence that Michelle hassuccessfully integrated these components. She like Rhonda also fails to identifydecomposers, which suggests that this group of organisms has been ‘lost’ in thetransition between taxonomic and ecological understandings. Interestingly,


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Michelle also depicts some as yet unassimilated knowledge about ‘crustal plateboundaries’.

In the first interview Michelle revealed some remarkably insightful thoughtsabout her own learning:

I: How do you feel about this [first] map now?

M: . . . . when I started the second map I could see the flaws in the first one, such as plantsnot being included in the different habitats. . . . I never really considered the abiotic factorsof the ocean . . . But in the very first chapter of the book we discuss the physical, geological,and chemical aspects of the ocean . . . and I think that is really important. . . . I think Icould still improve with consistency.

I: Let’s take a look at the second map then. Tell me everything you can about whatyou were thinking when you constructed this map.

M: . . . In the very beginning instead of branching off from ‘life in the ocean’ to ‘animals’and ‘plants’ I branch off to ‘biotic factors’ and ‘abiotic factors’, which I think is veryimportant. And then my plants are included in the different habitats . . . I think its a lotmore coherent and informative as far as what we’ve covered so far. But then again, oncewe get down here [pointing] I don’t really know a whole lot about the different phyla.

I: Looking at the two maps together, tell me how your ideas have changed.

M: Well, I’ve definitely changed my ideas about abiotic factors. . . . I think I was verynarrow minded toward marine biology. . . . It’s valuable to know that everything is inter-acting with each other. Nothing is really separate . . . It’s just one gigantic machine thatworks well due to all its different diverse parts. And I think that’s easy to see with conceptmaps.

I: Tell me how you study for this class.

M: I make note cards, because I have a very good memory. I tend to memorize things, but Idon’t just memorize them. I memorize the concepts also. You can’t just memorize aconcept, you have to know the concept, why does it work that way, how does it workwith other parts of that subject. . . . I think I understand the ideas and the concepts verywell. . . .

In her third and fourth concept maps (figure 8) Rhonda elaborates and providessome additional descriptive detail. However, the level of integration among theconcepts actually declines. A few abiotic issues seem to be thrown into the mapalmost at random (e.g. ‘air-water interface’ ‘stabilization’) but these factors are notreally used to explain much about significant ecological interactions in the marineenvironment. Most of the new additions are simply accretions of already existingconcepts, and the accretions merely add more examples of taxonomic knowledge(e.g. blue crab, rhodophyta, zooxanthellae, skeleton shrimp).

In the second and third interviews, Rhonda expresses some frustration withher own learning style:

I: [Second Interview] How do you feel about the [third] map now?

R: . . . I could have done a little bit better if I actually went back and looked over my notes.But this one was kind of a last minute thing, and I really didn’t get a chance to look overmy notes.

I: Tell me everything you can about what you were thinking when you constructedthe [third] map.

R: . . . it seems that the more that I’m doing this, then the less and less . . . you know, I’mtrying to put together; or I’m trying to think of things to put together, but the lessinformation that I can think of to . . . The more that I do this the less information thatI can think of to put on the maps because every time I look at something, I’m like ‘ohh, I’ve


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got this already’ and then I try to improve on one thing and try to connect it to others andit’s just making it harder. You know? Cause I can’t think of enough new things to put onthe map.

I: [Third Interview] . . . tell me everything you can about what you were thinkingwhen you did that [fourth] map.

R: The only thing I was thinking about was trying to put as much stuff on there as possibleand try to add on as much as possible from when I did it the first time. That’s about it.

I: How could you have improved on this map?

R: I have no idea. I mean, I have no idea. I mean, if I studied a little bit more, but I can’tsay that ’cause I just took a test right before then, so I knew as much information as I wasgoing to at the time.

I: Looking at the whole map, how have your ideas changed?

R: [Sighing] Not that much. I know that before I took the class I didn’t know very much,but I know that its changed . . . that I learned more and I retained a lot of the informationthat I learned all semester long.

A small portion of Michelle’s third and fourth concept maps is depicted in figure 9.The final map reveals an extensive, richly-detailed, and fairly well-integratedknowledge framework. Strikingly, however, there is virtually no indication ofthe role of decomposers in the marine environment. To contemporary biologists,decomposers and their role in nutrient cycling constitutes a pivotal and essentialconcept in ecology.

Despite the significant omission, however, the maps Michelle has drawn areremarkably complex, and she seems to understand this complexity and its signifi-cance:

I: [Second Interview] Looking at the two [second and third] maps together, how haveyour ideas changed?

M: . . . Every time I see the old map I say, Oh yeah, I can go into a lot more detail there,and I can go into a lot more detail there’, and then I try to fix those parts. So that’s howmy ideas would have changed. More detail. . . .

I: [Third Interview] How could you have improved on the [fourth] map?

M: I think I could have improved the map . . . by elaborating more on the different ideas,and also creating more links between the ideas.

I: Looking at this [fourth] map, how do you think your ideas have changed?

M: . . . the biggest thing that has changed with in my mind is that marine biology caninclude abiotic factors. I was just really dead-set on the fact that marine biology wasbiology and that was it. . . . It would just really be a big mistake to focus only on biology.. . . Everything on this map interacts with everything else, and if you left any of thoseabiotic factors out, you would be missing a gigantic portion of the ideas, I think.

Conclusions and implications

This study and its companion efforts (Pearsall et al. 1997) have attempted toexpand on a newly emergent and promising field of research and thinking inscience education; the field of knowledge restructuring and conceptual change.With several notable exceptions (Novak and Musonda 1991, Jones et al. 1999,Griffard and Wandersee 1999), work in this field has been largely speculative innature, and only recently have empirical studies begun to proliferate. To date,results of these preliminary studies seem to confirm findings of related work inthe cognitive sciences and the history and philosophy of science.


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Restructuring

The current study provides evidence that a substantial amount of knowledgerestructuring occurs in an ‘upper-level’ college biology course over a 16 weekperiod. The ‘structural complexity profiles’ suggest a stepwise, gradual, andcumulative growth in the knowledge base. Importantly, however, this growth isconcurrent with periods both of weak and radical change. It is significant that themost important changes involving the highest level of the knowledge frameworksare concentrated within the first 6 weeks of the course. This period accounts forabout 55% of the ‘strong restructuring’ events. Similar findings were reported byPearsall et al. (1997).

These findings suggest that science courses may be strongly ‘front-loaded’ andthat researchers need to take a very careful look at what teachers and students aredoing during the early phases of a learning episode. Classroom observations, cur-ricular reviews, and in depth interviews are needed in order to explore these issuesin greater depth.

A new finding based on the ‘interconnectedness’ index suggests that integra-tion of new concepts does not keep pace with overall growth of the knowledgeframework as the semester progresses. In fact, it appears that the level of coherencein the knowledge base declines substantially and progressively over the 16 weekperiod, and that students are adding concepts to their knowledge frameworks morerapidly than they are integrated, suggesting a significant amount of rote learning.If so, we might expect these levels of rote learning to place significant constraintson the ability of students to use knowledge in novel settings. This finding and itspotentially important implications need to be explored more fully in subsequentstudies.

Gender

In the current effort we found two gender-related effects: one, a main effect (i.e.crosslinks) and another, an interaction effect (i.e. branching). In both cases theeffects favoured females. Previously we concluded that ‘gender may be an import-ant mediator of meaningful learning’ (Pearsall et al. 1997) and that ‘gender differ-ences . . . tended to favour females.’ Interestingly, in the last pair of studies wefound only one instance where males excelled (i.e. crosslinks), and that occurredwithin a group of nonscience majors.

For those interested in ‘gender fair’ assessment practices, these findings mayhave substantial importance. This is especially so since the majority of studies ongender differences in the natural sciences tend to find effects favouring males.Perhaps concept mapping offers a way of ‘evening the score’ in normally male-dominated disciplines.

Metacognition

Our interviews with Rhonda and Michelle suggest that successful learners in thenatural sciences may excel in self-awareness, and ability to monitor, regulate andcontrol their own learning. These findings need careful replication and extendedstudy in large-scale research efforts.


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Both Rhonda and Michelle began the semester with some major conceptualdifficulties and omissions concerning the diversity of life. These ‘conceptual gaps’(Griffard and Wandersee 1999) were substantially unaffected by the course, andultimately constrained their understanding of some significant ecological concepts.The transition from a phylogenetic or taxonomic framework to an ecological fra-mework is an excellent example of the sort of ‘critical junctures’ in learning(Mintzes et al. 1998, Trowbridge and Wandersee 1994) that need careful attentionin subsequent research. Despite these conceptual problems, however, whenpressed to reflect on their own learning, Michelle demonstrated significant insightwhile Rhonda retreated into a series of rationalizations. Several examples will serveto support these assertions.

When asked how they might improve on a previous map, Rhonda andMichelle evidenced substantially divergent epistemological views. To Rhondaimprovement means ‘putting more categories.’ She viewed the task as one of ‘put-ting as much stuff on there as possible.’ This suggests a kind of accretion model ofknowledge; a model that lends itself well to a form of rote mode learning. Michelle,on the other hand, was able to discern ‘flaws’ in her own thinking and to isolatesome of her own conceptual gaps (e.g. ‘abiotic factors’). She recognized learning asa kind of knowledge restructuring and suggested that it involves seeking relation-ships and connections (e.g. ‘You can’t just memorize a concept. . . .’).

While Rhonda and Michelle seemed fully aware of their own learning styles,Rhonda seemed not to understand the constraints and implications of her approachto learning, and was unable to suggest a way out of her predicament when con-fronted by the disappointing results of her efforts (e.g. ‘I have no idea. I mean Ihave no idea. . .’). Although her difficulties are apparently compounded by timemanagement and organizational problems, she seems unwilling or unable tochange (e.g. ‘I thought about doing concept maps but. . . .’). In contrast,Michelle displayed strong confidence in her own abilities as a learner (e.g. ‘Ithink I could still improve with consistency’) together with a flexibility and will-ingness to change course when necessary (e.g. ‘I was really just dead set on thefact . . .). She also recognized this ability in herself, which suggests a sort of self-awareness that Rhonda lacks.

These preliminary findings support the view that many students lack thefundamental learning skills and metacognitive abilities that are essential to successin the ‘knowledge age’ (Mintzes et al. 2000). But more than that; students are oftenunaware of the limitations and constraints of their own learning styles, and theimplications of failing to acquire these fundamental skills. Efforts aimed at helpingstudents learn how to learn (Novak and Gowin 1984, Novak 1998) appear verypromising, but substantial energy and resources need to be invested in large scalestudies to demonstrate the promise of these approaches in a variety of sciencedisciplines at the college and university (as well as the K-12) levels. As withmany such suggestions, demonstrating the value of these approaches awaits achange in political ‘will’ and a commitment to reallocate significant resources toimportant educational problems.

References

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restructuring knowledge in biology: cognitive processes and metacognitive reflections

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