Proceedings of EDM2023
Published July 5, 2023 | Version v2
Conference paper Open

A Data Mining Approach for Detecting Collusion in Unproctored Online Exams

Authors/Creators

Contributors

  • 1. WestEd, USA
  • 2. EPFL, Switzerland
  • 3. Google Research and Indian Institute of Science, India

Description

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.

Files

2023.EDM-long-papers.1.pdf

Files (355.2 kB)

Name Size Download all
md5:e7dc421d40b5f1fa3b5fe827a0430903
355.2 kB Preview Download