A Data Mining Approach for Detecting Collusion in Unproctored Online Exams
This addresses the challenge of academic integrity in remote education for universities, but it is incremental as it applies existing data mining methods to a new context.
The researchers tackled the problem of detecting collusion in unproctored online exams by analyzing event log data from pandemic-era take-home exams, identifying groups of students with suspiciously similar exams and establishing a rule of thumb for evaluating such cases.
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.