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The integration of knowledge and experimentation strategies in understanding a physical system
Author(s) -
Schauble Leona,
Glaser Robert,
Raghavan Kalyani,
Reiner Miriam
Publication year - 1992
Publication title -
applied cognitive psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.719
H-Index - 100
eISSN - 1099-0720
pISSN - 0888-4080
DOI - 10.1002/acp.2350060405
Subject(s) - hierarchy , task (project management) , psychology , causal model , function (biology) , identity (music) , domain (mathematical analysis) , cognition , domain knowledge , cognitive science , cognitive psychology , physical system , artificial intelligence , computer science , engineering , medicine , mathematical analysis , physics , mathematics , pathology , quantum mechanics , evolutionary biology , neuroscience , economics , acoustics , market economy , biology , systems engineering
This study investigated how people integrate different kinds of knowledge in coming to understand the functions of components of a physical system. Twenty‐three undergraduates worked on a hypothesis‐testing task in which they constructed test circuits to decide the identity of electrical components hidden inside boxes. The students' problem‐solving was driven by qualitative causal models that included ideas about cause and effect, task demands, and functions of circuits and circuit components. There was a hierarchy of four causal models, with higher levels representing increasing understanding and supporting increasingly successful problem solution. These causal models were associated with characteristic experimentation strategies, including strategies for generating evidence, interpreting evidence, and managing memory requirements. Some of these strategies followed directly from the causal model a student held, whereas others appeared to be more general. The level of understanding a student eventually attained about this physical system was a function of both domain knowledge and proficiency in experimentation strategies.