# A Data Mining Approach for Detecting Collusion in Unproctored Online Exams

arXiv: https://arxiv.org/abs/2302.07014 · HTML: https://mlsanity.com/paper/2302.07014/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 25 references that are in the map.
- First posted on arXiv 14 Feb 2023.

## Builds on (most cited first, 1 of 1)

- [Towards digitalisation of summative and formative assessments in academic teaching of statistics](https://mlsanity.com/paper/1811.02391/) (2018)

## Abstract (arXiv)

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.