Good proctor or "Big Brother"? AI Ethics and Online Exam Supervision Technologies

Watching the watchers: bias and vulnerability in remote proctoring software

Educators are rapidly switching to remote proctoring and examination software for their testing needs, both due to the COVID-19 pandemic and the expanding virtualization of the education sector. State boards are increasingly utilizing these software for high stakes legal and medical licensing exams. Three key concerns arise with the use of these complex software: exam integrity, exam procedural fairness, and exam-taker security and privacy. We conduct the first technical analysis of each of these concerns through a case study of four primary proctoring suites used in U.S. law school and state …

Educators' Perspectives of Using (or Not Using) Online Exam Proctoring

The onset of the COVID-19 pandemic changed the landscape of education and led to increased usage of remote proctoring tools that are designed to monitor students when they take assessments outside the classroom. While prior work has explored students' privacy and security concerns regarding online proctoring tools, the perspective of educators is under explored. Notably, educators are the decision makers in the classrooms and choose which remote proctoring services and the level of observations they deem appropriate. To explore how educators balance the security and privacy of their students w…

考试监控系统：在线考试中的异常行为检测

Examining Monitoring System: Detecting Abnormal Behavior In Online Examinations

针对在线考试作弊问题，本文提出考试监控系统，通过实时检测学生异常行为辅助监考人员决策，展现出高准确率与高速度。

一种检测无监考在线考试中串通行为的数据挖掘方法

A Data Mining Approach for Detecting Collusion in Unproctored Online Exams

针对疫情期间无监考带回家考试的串通风险，本文利用事件日志数据挖掘学生答卷相似性，并通过有监考对照组建立识别异常相似可疑案例的经验法则。

Unique Exams: Designing assessments for integrity and fairness

Educators have faced new challenges in effective course assessment during the recent, unprecedented shift to remote online learning during the COVID-19 pandemic. In place of typical proctored, timed exams, instructors must now rethink their methodology for assessing course-level learning goals. Are exams appropriate---or even feasible---in this new online, open-internet learning environment? In this experience paper, we discuss the unique exams framework: our framework for upholding exam integrity and student privacy. In our Probability for Computer Scientists Course at an R1 University, we de…

在线考试中作弊检测与定位的多实例学习方法

Multiple Instance Learning for Cheating Detection and Localization in Online Examinations

针对在线考试中作弊行为罕见且特征利用不全的问题，提出基于多实例学习的CHEESE框架，融合姿态与背景特征，在三个数据集上验证有效性，OEP上帧级AUC达87.58%。