Chi, Michelene T.H.

Engle, Randi A

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UC MercedProceedings of the Annual Meeting of the Cognitive Science SocietyTitleCategorization of Emergent Processes by Students at Different levels of ExpertisePermalinkhttps://escholarship.org/uc/item/0rw099ptJournalProceedings of the Annual Meeting of the Cognitive Science Society, 24(24)ISSN1069-7977AuthorsEngle, Randi AChi, Michelene T.H.Publication Date2002 Peer reviewedeScholarship.org Powered by the California Digital LibraryUniversity of CaliforniaCategorization of Emergent Processes by Students at Different Levels of ExpertiseRandi A. Engle (RAEngle@pitt.edu)Michelene T. H. Chi (chi@pitt.edu)Learning Research and Development Center, University of Pittsburgh3939 O’Hara St., Pittsburgh, PA 15260 USAIntroductionChi and Roscoe (2002) proposed that one reason for thepersistence of scientific misconceptions is that studentsclassify scientific phenomena into incorrect ontologicalcategories. In particular, Chi (submitted) has hypothesizedthat many commonly misunderstood science concepts (likeevolution and electric current) are emergent processes inwhich a macro-level phenomena emerges from complexinteractions between entities at a micro-level. Rather thancorrectly categorizing them this way, students are thought tomiscategorize them as non-emergent processes in whichthere is a more direct relationship between the two levels.To test this hypothesis, we are conducting a studycomparing how participants at different levels of expertisecategorize science problems across domains, using a cardsorting task modeled on Chi, Feltovich & Glaser (1981).The prediction of Chi’s theory is that participants with moreexpertise will be more likely to distinguish emergent fromnon-emergent problems while those with less expertise willoften conflate them. Experts will also be more likely to referto the 11 features of the emergent schema (e.g., disjointness,parallelism; see Chi, submitted) in defining their categories.MethodParticipantsParticipants consisted of 10 undergraduate and 9 doctoralstudents from the biology, chemistry, or physicsdepartments at a local university. Undergraduates had allcompleted 1st year courses in biology, chemistry, andphysics. Eight were single majors in one of these disciplinesand two were double majors in biology and chemistry.Doctoral students were in their third year or above.MaterialsParticipants sorted 24 science problems, 8 drawn from eachdiscipline’s 1st year course. Within each discipline, halfwere emergent and half were non-emergent processes.ProcedureParticipants were asked to sort the problems into piles withsimilar mechanisms for linking the macro and micro levels.In the 1st sort, they were allowed to make as many piles asthey wished. In the 2nd sort, they were asked to divide thecards into just two piles. In the 3rd sort, the experimentersorted the cards into emergent vs. non-emergent processes,and participants were asked to infer the distinction. In allcases, participants were asked to explain the explanatorymechanism each pile represented and why each problem fit.ResultsAs a preliminary measure of the degree to whichparticipants distinguished emergent from non-emergentprocesses, we calculated a weighted average (by pile size)of the absolute difference between the number of emergentversus non-emergent processes in each pile by the numberof cards in the pile. For example, 3 piles—one with 5emergent & 5 non-emergent problems, a 2nd with 1emergent & 7 non-emergent problems, and a 3rd with 6emergent & 0 non-emergent problems—would get a scoreof [10(0/10) + 8(6/8) + 6(6/6)] / 24 = 12/24 = .50. A valueof 1 on this score represents perfect separation of emergentversus non-emergent processes while 0 represents perfect50/50 mixing. Mean separation scores were .58 for doctoralstudents, .53 for single majors, and .73 for double majors,although none of these differences are statistically reliable.A complicating factor is that participants sometimes put,for example, only emergent problems in a given pile forreasons unrelated to their emergence. Thus, we analyzedparticipants’ definitions of their categories to determinewhether they referred to any of the 11 features of theemergent schema. Doctoral students’ categories referred tomore emergent features (1.78) than single majors (0.13; t(9)= 3.08, p < .05). Double majors referred to even more ofthem (3.50), but more data is needed to see if this is reliable.DiscussionDoctoral students and double majors used more features ofthe emergent schema in sorting science problems than singlemajors, although they were no more likely than singlemajors to create piles that distinguished emergent and non-emergent processes. In future work, we will include in oursample more double majors as well as professors to furtherinvestigate the nature of expertise in emergent processes.ReferencesChi, M. T. H. (submitted).  “Causal” and “Emergent”Explanatory Mechanisms: Potential Schemas forOvercoming Misunderstandings in Science.Chi, M. T. H., Feltovich, P. J., & Glaser, R. (1981).Categorization and representation of physics problemsby experts and novices.  Cognitive Science, 5, 121-152.Chi, M. T. H., & Roscoe, R. (2002).  The processes andchallenges of conceptual change.  In M. Limon & L.Mason (Eds.), Reconsidering conceptual change: Issuesin theory and practice. Dordrecht, NL: Kluwer.

Categorization of Emergent Processes by Students at Different levels of Expertise

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