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Remote Online-exam Dishonesty in Medical School during COVID-19 Pandemic: A Real-world Case with a Multi-method Approach
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Abstract
The COVID-19 pandemic significantly impacted medical education, causing a shift towards online learning. However, this transition posed challenges in administering online assessments, particularly in proctoring and detecting academic misconduct. This study aimed to investigate the prevalence of academic misconduct among medical students during remote online examinations using hierarchical clustering and comparing self-reported confessions and recorded video reviews. The results of the study confirmed the existence of academic misconduct in remote online examinations, as evidenced by both self-report and video footage review. Hierarchical clustering was able to predict academic misconduct with an accuracy rate of approximately 55%. These findings highlight the need for medical educators to develop and implement ethics and morality-focused courses to address the issues of dishonesty among medical students in online assessments. In conclusion, this study shed light on the challenges of remote online assessments in medical education, particularly in detecting academic misconduct. Using a data-driven approach to analyse the prevalence of academic misconduct, this work provided insights into the need for ethical and moral education in medical curricula, which can help address the dishonesty among medical students during online assessments.
Springer Science and Business Media LLC
Title: Remote Online-exam Dishonesty in Medical School during COVID-19 Pandemic: A Real-world Case with a Multi-method Approach
Description:
Abstract
The COVID-19 pandemic significantly impacted medical education, causing a shift towards online learning.
However, this transition posed challenges in administering online assessments, particularly in proctoring and detecting academic misconduct.
This study aimed to investigate the prevalence of academic misconduct among medical students during remote online examinations using hierarchical clustering and comparing self-reported confessions and recorded video reviews.
The results of the study confirmed the existence of academic misconduct in remote online examinations, as evidenced by both self-report and video footage review.
Hierarchical clustering was able to predict academic misconduct with an accuracy rate of approximately 55%.
These findings highlight the need for medical educators to develop and implement ethics and morality-focused courses to address the issues of dishonesty among medical students in online assessments.
In conclusion, this study shed light on the challenges of remote online assessments in medical education, particularly in detecting academic misconduct.
Using a data-driven approach to analyse the prevalence of academic misconduct, this work provided insights into the need for ethical and moral education in medical curricula, which can help address the dishonesty among medical students during online assessments.
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