# A Visual Analytics Approach to Facilitate the Proctoring of Online Exams

arXiv: https://arxiv.org/abs/2101.07990 · HTML: https://mlsanity.com/paper/2101.07990/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jan 2021.

## Built on by (most cited first, 5 of 5)

- [Where Are We So Far? Understanding Data Storytelling Tools from the Perspective of Human-AI Collaboration](https://mlsanity.com/paper/2309.15723/) (2023)
- [VideoPro: A Visual Analytics Approach for Interactive Video Programming](https://mlsanity.com/paper/2308.00401/) (2023)
- [Multiple Instance Learning for Cheating Detection and Localization in Online Examinations](https://mlsanity.com/paper/2402.06107/) (2024)
- [Interactive Hybrid Rice Breeding with Parametric Dual Projection](https://mlsanity.com/paper/2507.11848/) (2025)
- [A Two-Stage, Object-Centric Deep Learning Framework for Robust Exam Cheating Detection](https://mlsanity.com/paper/2604.16234/) (2026)

## Abstract (arXiv)

Online exams have become widely used to evaluate students' performance in mastering knowledge in recent years, especially during the pandemic of COVID-19. However, it is challenging to conduct proctoring for online exams due to the lack of face-to-face interaction. Also, prior research has shown that online exams are more vulnerable to various cheating behaviors, which can damage their credibility. This paper presents a novel visual analytics approach to facilitate the proctoring of online exams by analyzing the exam video records and mouse movement data of each student. Specifically, we detect and visualize suspected head and mouse movements of students in three levels of detail, which provides course instructors and teachers with convenient, efficient and reliable proctoring for online exams. Our extensive evaluations, including usage scenarios, a carefully-designed user study and expert interviews, demonstrate the effectiveness and usability of our approach.