A Visual Analytics Approach to Facilitate the Proctoring of Online Exams

TL;DR

This paper introduces a visual analytics system that enhances online exam proctoring by analyzing video and mouse data to detect suspicious behaviors, improving reliability and efficiency in remote assessments.

Contribution

It presents a novel visual analytics approach that visualizes student behaviors to assist instructors in proctoring online exams more effectively.

Findings

Effective detection of suspicious head and mouse movements.

User study confirms improved proctoring efficiency.

Expert feedback supports usability and reliability.

Abstract

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.