A Data Mining Approach for Detecting Collusion in Unproctored Online Exams

descriptionPublicationkeyboard_double_arrow_right Article , Conference object , Preprint 01 Jan 2023Embargo end date: 01 Jan 2023Publisher:International Educational Data Mining SocietyJournal:CoRR, volume abs/2302.07014

Authors: Janine Langerbein; Till Massing; Jens Klenke; Natalie Reckmann; Michael Striewe; Michael Goedicke; Christoph Hanck;

doi: 10.5281/zenodo.8115648 , 10.48550/arxiv.2302.07014 , 10.5281/zenodo.8157692 , 10.5281/zenodo.8115649

arXiv: 2302.07014

A Data Mining Approach for Detecting Collusion in Unproctored Online Exams

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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 comparison group. By this, we establish a rule of thumb for evaluating which cases are ``outstandingly similar'', i.e., suspicious cases.

Related Organizations

University of Duisburg-Essen
Germany

Keywords

FOS: Computer and information sciences, Computer Science - Computers and Society, Computer Science - Machine Learning, Computers and Society (cs.CY), Machine Learning (cs.LG)

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