  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.
