A Visual Analytics Approach to Facilitate the Proctoring of Online Exams
Haotian Li , Min Xu , Yong Wang , Huan Wei , Huamin Qu
Our approach provides novel visualizations for proctors to identify cheating cases. (a) Student List View is an overview of all students’ risk of cheating in an online exam. (b) Question List View shows the risk level of all questions finished by a student. (c) Behavior View presents a student’s detailed head and mouse movements while answering a question. (c1) and (c2) are the upper detailed behavior chart and the suspected case chart, respectively. (d) Playback View enables proctors to check raw videos and animated visualization of mouse movements on the exam web page. (e) The control panel can be used to select online exams and adjust several parameters by proctors. (f) provides a function to save screenshots of the raw video. Screenshots in (f) are taken at time points indicated by vertical black dashed lines in (c). (g1)-(g4) illustrate fast location and convenient verification of cheating behaviors in Usage Scenario 1.
Description
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.
Publication
Haotian Li, Min Xu, Yong Wang, Huan Wei, and Huamin Qu. 2021. A Visual Analytics Approach to Facilitate the Proctoring of Online Exams. In CHI Conference on Human Factors in Computing Systems (CHI ’21), May 8–13, 2021, Yokohama, Japan. ACM, New York, NY, USA, 17 pages. https://doi.org/10.1145/3411764.3445294.