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A Detailed Characterization of the Expert Problem-Solving Process in Science and Engineering: Guidance for Teaching and Assessment

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Τίτλος: A Detailed Characterization of the Expert Problem-Solving Process in Science and Engineering: Guidance for Teaching and Assessment
Γλώσσα: English
Συγγραφείς: Price, Argenta M., Kim, Candice J., Burkholder, Eric W., Fritz, Amy V., Wieman, Carl E.
Πηγή: CBE - Life Sciences Education. Sep 2021 20(3).
Διαθεσιμότητα: American Society for Cell Biology. 8120 Woodmont Avenue Suite 750, Bethesda, MD 20814-2762. Tel: 301-347-9300; Fax: 301-347-9310; e-mail: ascbinfo@ascb.org; Website: http://www.ascb.org
Peer Reviewed: Y
Page Count: 15
Ημερομηνία έκδοσης: 2021
Τύπος εγγράφου: Journal Articles
Reports - Research
Descriptors: Problem Solving, Science Education, Engineering Education, Teaching Methods, Professional Personnel, Protocol Analysis, Decision Making
DOI: 10.1187/cbe.20-12-0276
ISSN: 1931-7913
Περίληψη: A primary goal of science and engineering (S&E) education is to produce good problem solvers, but how to best teach and measure the quality of problem solving remains unclear. The process is complex, multifaceted, and not fully characterized. Here, we present a detailed characterization of the S&E problem-solving process as a set of specific interlinked decisions. This framework of decisions is empirically grounded and describes the entire process. To develop this, we interviewed 52 successful scientists and engineers ("experts") spanning different disciplines, including biology and medicine. They described how they solved a typical but important problem in their work, and we analyzed the interviews in terms of decisions made. Surprisingly, we found that across all experts and fields, the solution process was framed around making a set of just 29 specific decisions. We also found that the process of making those discipline-general decisions (selecting between alternative actions) relied heavily on domain-specific predictive models that embodied the relevant disciplinary knowledge. This set of decisions provides a guide for the detailed measurement and teaching of S&E problem solving. This decision framework also provides a more specific, complete, and empirically based description of the "practices" of science.
Abstractor: As Provided
Entry Date: 2021
Αριθμός Καταχώρησης: EJ1310945
Βάση Δεδομένων: ERIC
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  Data: A Detailed Characterization of the Expert Problem-Solving Process in Science and Engineering: Guidance for Teaching and Assessment
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  Data: A primary goal of science and engineering (S&E) education is to produce good problem solvers, but how to best teach and measure the quality of problem solving remains unclear. The process is complex, multifaceted, and not fully characterized. Here, we present a detailed characterization of the S&E problem-solving process as a set of specific interlinked decisions. This framework of decisions is empirically grounded and describes the entire process. To develop this, we interviewed 52 successful scientists and engineers ("experts") spanning different disciplines, including biology and medicine. They described how they solved a typical but important problem in their work, and we analyzed the interviews in terms of decisions made. Surprisingly, we found that across all experts and fields, the solution process was framed around making a set of just 29 specific decisions. We also found that the process of making those discipline-general decisions (selecting between alternative actions) relied heavily on domain-specific predictive models that embodied the relevant disciplinary knowledge. This set of decisions provides a guide for the detailed measurement and teaching of S&E problem solving. This decision framework also provides a more specific, complete, and empirically based description of the "practices" of science.
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      – SubjectFull: Protocol Analysis
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      – SubjectFull: Decision Making
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