Papers › A Data Mining Approach for Detecting Collusion in Unproctored Online Exams
A Data Mining Approach for Detecting Collusion in Unproctored Online Exams
Janine Langerbein, Till Massing, Jens Klenke, Natalie Reckmann, Michael Striewe, Michael Goedicke, Christoph Hanck
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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