A Data Mining Approach for Detecting Collusion in Unproctored Online Exams

Janine Langerbein, Till Massing, Jens Klenke, Natalie Reckmann, Michael Striewe +2 more
2/14/2023
cs.CYcs.LG

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

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Cite this paper

@article{langerbein2023data,
  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},
  year   = {2023},
  eprint = {2302.07014},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2302.07014v3}
}

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