2025 ASEE Annual Conference & Exposition
BOARD # 88: WIP: Detecting Academic Dishonesty in Online Exams Using Machine Learning Techniques
Presented at Computers in Education Division (COED) Poster Session (Track 1.A)
The COVID-19 pandemic has increased the need for robust methods to uphold academic integrity in online examinations, where issues like impersonation and cheating are prevalent. Most machine learning techniques rely on image or video data, but few are looking at other indicators, such as post-score analysis. This study assesses how proficient machine learning models, such as Random Forest, Support Vector Machine, Logistic Regression, and Gradient Boosting, identify suspicious behaviors based on response accuracy and timing on exam datasets. The Gradient Boosting model achieved the best performance with an accuracy of 97.99% and an F1 score of 98.56%, highlighting the viability of post-score analysis for scalable and reliable academic integrity detection. These findings emphasize the potential of post-score analysis to safeguard the integrity of online education through effective and trustworthy detection techniques.
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Sumaya Binte Zilani Choya George Mason University
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