Three Psychometric-Model-Based Option-Scored Multiple Choice Item Design Principles that Enhance Instruction by Improving Quiz Diagnostic Classification of Knowledge Attributes

William Stout (), Robert Henson and Lou DiBello
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William Stout: University of Illinois at Urbana-Champaign: (Statistics: Emeritus)
Robert Henson: University of North Carolina Greensboro (Education)
Lou DiBello: UNIVERSITY OF ILLINOIS CHICAGO (LEARNING SCIENCES RESEARCH INSTITUTE: EMERITUS)

Psychometrika, 2023, vol. 88, issue 4, No 8, 1299-1333

Abstract: Abstract Three IRT diagnostic-classification-modeling (DCM)-based multiple choice (MC) item design principles are stated that improve classroom quiz student diagnostic classification. Using proven-optimal maximum likelihood-based student classification, example items demonstrate that adherence to these item design principles increases attribute (skills and especially misconceptions) correct classification rates (CCRs). Simple formulas compute these needed item CCRs. By use of these psychometrically driven item design principles, hopefully enough attributes can be accurately diagnosed by necessarily short MC-item-based quizzes to be widely instructionally useful. These results should then stimulate increased use of well-designed MC item quizzes that target accurately diagnosing skills/misconceptions, thereby enhancing classroom learning.

Keywords: Generalized Diagnostic Classifcation Modeling (GDCM); Extended RUM (ERUM); formative assessment; optimal student diagnostic classification; Optimal multiple choice (MC) item (question) design; skills and misconceptions diagnosis (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11336-022-09885-3

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