Integrity Breach Detection Using Machine Learning Algorithms
Jennifer Landes , Sonja KÃ¶ppl  and Meike Klettke
Digital Signal Processing and Artificial Intelligence for Automatic Learning, Vol. 3, No. 4, 2024.
DOI: https://doi.org/10.6025/dspaial/2024/3/4/135-146
Link: https://www.dline.info/dspai/fulltext/v3n4/dspaiv3n4_3.pdf
Abstract:
Academic honesty in higher education can be affected by personal or organizational
factors. Engaging in dishonest practices diminishes the integrity of the educational
setting and can lead to adverse outcomes for individual learners and the academic
community. To investigate the elements that contribute to studentsâ€™ cheating behaviors, a quantitative research study was undertaken, concentrating on the types
of assessments and tasks that are most vulnerable to cheating. The data gathered
has been analyzed using Machine Learning techniques, and the findings have been
presented visually. This research is part of a dissertation project, and the survey
outcomes will be utilized for a study involving eye-tracking to assess studentsâ€™
cheating behaviors. The long-term objective is to create online examination methods resistant to specific cheating tactics.