Machine learning for autonomous online exam fraud detection: A concept design

Abstract

E-learning (EL) has emerged as one of the most valuable means for continuing education around the world, especially in the aftermath of the global pandemic that included a variety of obstacles. Real-time online assessments have become a significant concern for educational organizations. Instances of fraudulent behavior during online exams (OEs) have created considerable challenges for exam invigilators, who are unable to identify and remove such dishonest behavior. In response to this significant issue, educational institutions have used a variety of manual procedures to alleviate the situation, but none of these measures have shown to be particularly innovative or effective. The current study presents a novel strategy for detecting fraudulent actions in real time during OEs that uses convolutional neural network (CNN) algorithms and image processing. The development model will be trained using the CK and CK++ datasets. The training procedure will use 80% of the selected dataset, with the remaining 20% used for model testing to confirm the model’s efficacy and generalization capacity. This project intends to revolutionize the monitoring and prevention of fraudulent actions during online tests by integrating CNN techniques and image processing. The use of CK and CK++ datasets, as well as an 80–20 split for training and testing, contributes to the study’s thorough and rigorous approach. Educational institutions can improve their assessment procedures and maintain the credibility of EL as a credible and equitable way of continuing education by successfully using this unique technique.