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The self-assessment dilemma: an open-source, ethical method using Matlab to formulate multiple-choice quiz questions for online reinforcement.

Harry J WitchelJoseph H GuppyClaire F Smith
Published in: Advances in physiology education (2019)
Student self-assessment using computer-based quizzes has been shown to increase subject memory and engagement. Some types of self-assessment quizzes can be associated with a dilemma between 1) medical students who want the self-assessment quiz to be clearly related to upcoming summative assessments or curated by the exam-setters, and 2) university administrators and ethics committees who want clear guarantees that the self-assessment quizzes are not based on the summative assessments or made by instructors familiar with the exam bank of items. An algorithm in Matlab was developed to formulate multiple-choice questions for both ion transport proteins and pharmacology. A resulting question/item subset was uploaded to the Synap online self-quiz web platform, and 48 year 1 medical students engaged with it for 3 wk. Anonymized engagement statistics for students were provided by the Synap platform, and a paper-based exit questionnaire with an 80% response rate ( n = 44) measured satisfaction. Four times as many students accessed the quiz system via laptop compared with phone/tablet. Of 391 questions/items, over 11,749 attempts were made. Greater than 80% of respondents agreed with each of the positive statements (ease of use, enjoyed, engaged more, learned more, and wanted it to be extended to other modules). Despite simplistic questions and rote memorization, the questions developed by this system were engaged with and were received positively. Students strongly supported extending the system.
Keyphrases
  • medical students
  • public health
  • machine learning
  • deep learning
  • healthcare
  • health information
  • big data
  • single cell
  • cross sectional