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STEM exam performance: Open- versus closed-book methods in the large language model era.

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Area of Science:

  • STEM Education
  • Educational Technology
  • Academic Integrity

Background:

  • The COVID-19 pandemic accelerated remote learning, increasing scrutiny of open-book examinations (OBEs) versus closed-book examinations (CBEs) in STEM.
  • Sophisticated large language models (LLMs) are emerging as a significant factor in educational assessment.

Purpose of the Study:

  • To evaluate the efficacy of OBEs compared to CBEs on student performance and perceptions within STEM subjects.
  • To consider the influence of LLMs on the validity of OBEs.

Main Methods:

  • Systematic review of peer-reviewed articles published from 2013, adhering to PRISMA guidelines.
  • Meta-analysis of eight studies using random effects models to assess standardized mean differences.
  • Heterogeneity evaluated using I-squared statistics, Cochrane's Q test, and Tau statistics.

Main Results:

  • OBEs generally resulted in better student scores than CBEs, though significant heterogeneity was observed (I² = 97%).
  • Observational studies indicated more pronounced effects of OBEs, alongside concerns regarding technical difficulties and cheating.

Conclusions:

  • OBEs assess competencies better aligned with current educational paradigms than CBEs.
  • The emergence of LLMs presents challenges to OBE validity and academic integrity, necessitating further investigation.
  • Institutions must prudently consider OBE competencies in light of evolving technology and ensure fair student evaluations.