@article{4344,
  author = {Jennifer Landes , Sonja Köppl  and Meike Klettke},
  title = {Integrity Breach Detection Using Machine Learning Algorithms},
  journal = {Digital Signal Processing and Artificial Intelligence for Automatic Learning},
  year = {2024},
  volume = {3},
  number = {4},
  doi = {https://doi.org/10.6025/dspaial/2024/3/4/135-146},
  url = {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 students’ 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 students’
cheating behaviors. The long-term objective is to create online examination methods resistant to specific cheating tactics.},
}