Abstract
Online education is important in the COVID-19 pandemic, but online exam at individual homes invites students to cheat in various ways, especially collusion. While physical proctoring is impossible during social distancing, online proctoring is costly, compromises privacy, and can lead to prevailing collusion. Here we develop an optimization-based anti-collusion approach for distanced online testing (DOT) by minimizing the collusion gain, which can be coupled with other techniques for cheating prevention. With prior knowledge of student competences, our DOT technology optimizes sequences of questions and assigns them to students in synchronized time slots, reducing the collusion gain by 2–3 orders of magnitude relative to the conventional exam in which students receive their common questions simultaneously. Our DOT theory allows control of the collusion gain to a sufficiently low level. Our recent final exam in the DOT format has been successful, as evidenced by statistical tests and a post-exam survey.
Highlights
Testing is essential for measuring and improving educational outcomes[1], but a major concern is that many students tend to cheat[2,3]
As far as the assessment of learning outcomes is concerned, social distancing works directly against proctoring[13] since online testing performed at individual homes creates more chances to cheat[14] and increases temptation to do so[15,16,17]
How to proctor online exams presents a new challenge during social distancing[6], as conventional approaches do not take the pandemic into account[14]
Summary
Testing is essential for measuring and improving educational outcomes[1], but a major concern is that many students tend to cheat[2,3]. The anti-collusion exam design can efficiently reduce the collusion gain mainly due to following reasons (Fig. 1b–d): (1) The maximum question leakage from top to down of C consecutive cyclic sequences can be reduced to zero if M2 − M1 + 1 ≥ C (Supplementary Fig. 1); (2) by grouping, the equivalent number of students (the number of groups) can be significantly reduced to just use the C sequences; (3) students with similar competences have small probabilities to cheat within their group due to the fact that they can only obtain tiny collusion gains, the intra-group collusion is facilitated because of the same sequence shared With this procedure, by making C = M2 − M1 + 1 sufficiently large we can control the maximum individual collusion gain as well as the average collusion gain below any desired level. Our main idea is to optimally deliver collusion control, but it is usually not optimal since it does not questions to students as individual-specific sequences in a fully take advantage of the knowledge of students’ competences
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
