Journal Article

A machine learning assistant for detecting fraudulent activities in synchronous online programming exams

Quiroga, Jose, Garcia, Miguel, Ortin, Francisco, Gago, Alonso

2025

Overview

The rapid expansion of online learning has made education more accessible but has also introduced significant challenges in maintaining academic integrity, particularly during online exams. For certain types of exams, students are prohibited from connecting to the Internet to prevent them from accessing unauthorized resources, utilizing generative artificial intelligence tools, or engaging in other forms of cheating. However, in online exams, students must remain connected to the Internet. Most existing online proctoring systems rely on various devices to monitor students’ actions and environments during the exam, focusing on tracking physical behavior, such as facial expressions, eye movements, and the presence of unauthorized materials, rather than analyzing the students’ work within their computers. This often requires human review to determine whether students are engaging in unauthorized actions. This article presents the development and evaluation of a machine-learning-based assistant designed to assist instructors in detecting fraudulent activities in real-time during online programming exams. Our system leverages a convolutional neural network (CNN) followed by a recurrent neural network (RNN) and a dense layer to analyze sequences of screenshot frames captured from students’ screens during exams. The system achieves an accuracy of 95.18% and an F 2 -score of 94.2%, prioritizing recall to emphasize detecting cheating instances, while minimizing false positives. Notably, data augmentation and class-weight adjustments during training significantly enhanced the model’s performance, while transfer learning and alternative loss functions did not provide additional improvements. In post-deployment feedback, instructors expressed high satisfaction with the system’s ability to assist in the rapid detection of cheating, reinforcing the potential of machine learning to support real-time monitoring in large-scale online exams.

• Publisher: PeerJ. Ltd,PeerJ, Inc,PeerJ Inc
• Subject: Artificial Intelligence / Artificial neural networks / Cameras / Cheating / Computer Education / Computers / Convolutional neural network / Data augmentation / Data Mining and Machine Learning / Deep learning / Distance learning / Education / Evaluation / Eye movements / Flipped classroom / Fraud / Fraudulent activities / Generative artificial intelligence / Human-Computer Interaction / Infrastructure / Internet / Machine learning / Neural networks / On-line programming / Online synchronous exams / Programming exams / Real time / Recurrent neural network / Recurrent neural networks / Sequences / Student behavior / Students / Teachers / Virtual private networks / Webcams