Academic Fraud Detection in Online Exams with DNN's Multilayer Model - Erdem, Bahaddin; Karabatak, Murat (Institute of Electrical and Electronics Engineers Inc., 2025)
This study aims to reveal the best deep learning models that are improved and optimized by predicting undesirable behavior patterns using a dataset consisting of artificial and real exam data of students taking online distance education courses in an online environment through the distance education system. Using online exam data of 129 students, the researchers conducted analysis with two different scenarios to determine the best prediction performance through regression and classification models. The model we proposed was determined as a four-layer DNN with 80.4% test performance in detecting students who 'cheated' from undesirable behavior patterns, which was performed with K-10, K-5 and K-3 cross-validation. The results prove that students' online distance education exam data can be easily applied to the DNN model. The models presented in the study provide a roadmap for educational institutions to evaluate their online examination practices and develop more effective strategies for academic honesty. © 2025 IEEE.
13th International Symposium on Digital Forensics and Security, ISDFS 2025 -- 24 April 2025 through 25 April 2025 -- Boston -- 209331
Keywords: Academic Honesty; Cheating; Deep Learning; Online Exam