Early prediction of medical students' performance in high-stakes examinations using machine learning approaches.

ABSTRACT:

Introduction

Since the advent of medical education systems, managing high-stakes exams has been a top priority and challenge for all policymakers. However, considering machine learning (ML) techniques as a replacement for medical licensing examinations, particularly during crises such as the COVID-19 outbreak, could be an effective solution. This study uses ML models to develop a framework for predicting medical students' performance on high-stakes exams, such as the Comprehensive Medical Basic Sciences Examination (CMBSE).

Material and methods

Prediction of students' status and score on high-stakes examinations faces several challenges, including an imbalanced number of failing and passing students, a large number of heterogeneous and complex features, and the need to ident

SUBMITTER: Mastour H 

PROVIDER: S-EPMC10372649 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature