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

Author

Listed:
  • William Stout

    (University of Illinois at Urbana-Champaign: (Statistics: Emeritus)
    UNIVERSITY OF ILLINOIS CHICAGO (LEARNING SCIENCES RESEARCH INSTITUTE: EMERITUS))

  • Robert Henson

    (University of North Carolina Greensboro (Education))

  • Lou DiBello

    (UNIVERSITY OF ILLINOIS CHICAGO (LEARNING SCIENCES RESEARCH INSTITUTE: EMERITUS))

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.

Suggested Citation

  • William Stout & Robert Henson & Lou DiBello, 2023. "Three Psychometric-Model-Based Option-Scored Multiple Choice Item Design Principles that Enhance Instruction by Improving Quiz Diagnostic Classification of Knowledge Attributes," Psychometrika, Springer;The Psychometric Society, vol. 88(4), pages 1299-1333, December.
    • Handle: RePEc:spr:psycho:v:88:y:2023:i:4:d:10.1007_s11336-022-09885-3
    DOI: 10.1007/s11336-022-09885-3
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