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Assessment of biomedical engineering knowledge using true-false questions.

Manish Sreenivasa1, Lucy Armitage1, Winson C C Lee2

  • 1School of Mechanical, Materials, Mechatronic and Biomedical Engineering, Faculty of Engineering and Information Sciences, University of Wollongong, Wollongong, NSW, 2522, Australia.

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Summary

True-false (TF) questions are effective for online, unsupervised biomedical engineering exams. They show high score variation, differentiating students and predicting overall performance in remote assessments.

Keywords:
Closed-ended questionsProctoringRemote learningStudent outcomes

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Area of Science:

  • Biomedical Engineering Education
  • Educational Technology
  • Assessment Methods

Background:

  • COVID-19 pandemic necessitated a shift to remote online examinations.
  • Online, unsupervised exams present invigilation challenges.
  • Closed-ended formats like true-false (TF) suit automated scoring.

Purpose of the Study:

  • To investigate the efficacy of TF questions in online, unsupervised undergraduate Biomedical Engineering examinations.
  • To analyze score characteristics of TF questions compared to other formats under remote conditions.

Main Methods:

  • Analysis of TF and other question-type scores from 57 students across three online, unsupervised examinations in 2020.
  • Calculation of coefficient of variance (CV) for TF and other question types.
  • Correlation analysis between TF scores and overall subject scores.

Main Results:

  • TF questions exhibited a significantly higher CV (42.7%) compared to other question types (22.3%).
  • High CV in TF questions attributed to unanswered (13.3% ± 17.2%) and incorrectly guessed (16.4% ± 11.5%) responses.
  • Significant positive correlation (r=0.64, p<0.05) found between TF scores and overall subject scores.

Conclusions:

  • TF questions are valuable for assessing biomedical content in online, unsupervised settings.
  • The high score variation in TF questions aids in differentiating student performance.
  • TF questions demonstrate effectiveness as predictors of overall student achievement in remote assessments.