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Using cognitive models to develop quality multiple-choice questions.

Debra Pugh1, Andre De Champlain2, Mark Gierl3

  • 1a Department of Medicine , The Ottawa Hospital, University of Ottawa , Ottawa , Ontario , Canada ;

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Summary
This summary is machine-generated.

Automatic item generation (AIG) offers an efficient solution for creating multiple-choice questions (MCQs) in competency-based education. This method uses cognitive models to develop high-quality assessments comparable to traditional approaches.

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Area of Science:

  • Educational Assessment
  • Cognitive Science
  • Medical Education

Background:

  • Competency-based education necessitates increased assessment opportunities.
  • Traditional exam content development is labor-intensive.
  • Automatic item generation (AIG) offers an innovative solution for creating multiple-choice questions (MCQs).

Purpose of the Study:

  • To introduce a novel framework for developing high-quality MCQs.
  • To leverage cognitive models for efficient and effective MCQ development.
  • To improve traditional committee-based MCQ development processes.

Main Methods:

  • Utilizing computer technology to generate test items from cognitive models.
  • Deconstructing clinical reasoning processes by content experts to build cognitive models.
  • Applying linear programming principles to enhance traditional MCQ development.

Main Results:

  • AIG demonstrates efficiency in generating assessment items.
  • MCQs developed using cognitive models exhibit psychometric properties comparable to traditional methods.
  • The approach facilitates the assessment of higher-order thinking skills, such as knowledge application.

Conclusions:

  • Cognitive models provide a framework for efficient, high-quality MCQ development.
  • This approach enhances traditional assessment creation processes.
  • It is a viable method for assessing complex cognitive skills in educational settings.