What Can Be Learned from a Laboratory Model of Conceptual Change? Descriptive Findings and Methodological Issues

Autor/inn/en	Ohlsson, Stellan; Cosejo, David G.
Titel	What Can Be Learned from a Laboratory Model of Conceptual Change? Descriptive Findings and Methodological Issues
Quelle	In: Science & Education, 23 (2014) 7, S.1485-1504 (20 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0926-7220
DOI	10.1007/s11191-013-9658-6
Schlagwörter	Cognitive Processes; Scientific Concepts; Change Strategies; Concept Formation; Research Methodology; Science Laboratories; Models; Classification; Experiments + Suchen Sie Ihr Suchwort? Cognitive process; Kognitiver Prozess; Lösungsstrategie; Concept learning; Begriffsbildung; Research method; Forschungsmethode; Analogiemodell; Classification system; Klassifikation; Klassifikationssystem; Erprobung
Abstract	The problem of how people process novel and unexpected information--"deep learning" (Ohlsson in "Deep learning: how the mind overrides experience." Cambridge University Press, New York, 2011)--is central to several fields of research, including creativity, belief revision, and conceptual change. Researchers have not converged on a single theory for conceptual change, nor has any one theory been decisively falsified. One contributing reason is the difficulty of collecting informative data in this field. We propose that the commonly used methodologies of historical analysis, classroom interventions, and developmental studies, although indispensible, can be supplemented with studies of laboratory models of conceptual change. We introduce "re-categorization," an experimental paradigm in which learners transition from one definition of a categorical concept to another, incompatible definition of the same concept, a simple form of conceptual change. We describe a re-categorization experiment, report some descriptive findings pertaining to the effects of category complexity, the temporal unfolding of learning, and the nature of the learner's final knowledge state. We end with a brief discussion of ways in which the re-categorization model can be improved. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10