Multiple-Choice Tests and Student Understanding: What Is the Connection?

Autor/inn/en	Simkin, Mark G.; Kuechler, William L.
Titel	Multiple-Choice Tests and Student Understanding: What Is the Connection?
Quelle	In: Decision Sciences Journal of Innovative Education, 3 (2005) 1, S.73-98 (26 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1540-4595
DOI	10.1111/j.1540-4609.2005.00053.x
Schlagwörter	Student Evaluation; Multiple Choice Tests; Responses; Test Items; Comprehension; Evaluation Methods; Test Format; Hypothesis Testing + Suchen Sie Ihr Suchwort? Schulnote; Studentische Bewertung; Multiple choice examinations; Multiple-choice tests, Multiple-choice examinations; Multiple-Choice-Verfahren; Test content; Testaufgabe; Verstehen; Verständnis; Testentwicklung; Hypothesenprüfung; Hypothesentest
Abstract	Instructors can use both "multiple-choice" (MC) and "constructed response" (CR) questions (such as short answer, essay, or problem-solving questions) to evaluate student understanding of course materials and principles. This article begins by discussing the advantages and concerns of using these alternate test formats and reviews the studies conducted to test the hypothesis (or perhaps better described as the hope) that MC tests, by themselves, perform an adequate job of evaluating student understanding of course materials. Despite research from educational psychology demonstrating the potential for MC tests to measure the same levels of student mastery as CR tests, recent studies in specific educational domains find imperfect relationships between these two performance measures. We suggest that a significant confound in prior experiments has been the treatment of MC questions as homogeneous entities when in fact MC questions may test widely varying levels of student understanding. The primary contribution of the article is a modified research model for CR/MC research based on knowledge-level analyses of MC test banks and CR question sets from basic computer language programming. The analyses are based on an operationalization of Bloom's Taxonomy of Learning Goals for the domain, which is used to develop a skills-focused taxonomy of MC questions. However, we propose that their analyses readily generalize to similar teaching domains of interest to decision sciences educators such as modeling and simulation programming. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01