A Data Mining Approach for Detecting Collusion in Unproctored Online Exams
Computers and Society
2023-08-21 v3 Machine Learning
Abstract
Due to the precautionary measures during the COVID-19 pandemic many universities offered unproctored take-home exams. We propose methods to detect potential collusion between students and apply our approach on event log data from take-home exams during the pandemic. We find groups of students with suspiciously similar exams. In addition, we compare our findings to a proctored control group. By this, we establish a rule of thumb for evaluating which cases are "outstandingly similar", i.e., suspicious cases.
Keywords
Cite
@article{arxiv.2302.07014,
title = {A Data Mining Approach for Detecting Collusion in Unproctored Online Exams},
author = {Janine Langerbein and Till Massing and Jens Klenke and Natalie Reckmann and Michael Striewe and Michael Goedicke and Christoph Hanck},
journal= {arXiv preprint arXiv:2302.07014},
year = {2023}
}