Observations of Cheating Behaviours in Online Examinations and Tools for Mitigation
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paper presented at the FIE 2023 conference from Manuel Castro∗, Sathiamoorthy Manoharan†, Ulrich Speidel†, Xinfeng Ye†, and Jiayi Zu† ∗ Universidad Nacional de Educación a Distancia (UNED) † University of Auckland
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Observations of Cheating Behaviours in Online Examinations and Tools for Mitigation
- 1. Observations of Cheating Behaviours in Online Examinations and Tools for Mitigation Manuel Castro∗, Sathiamoorthy Manoharan† Ulrich Speidel†, Xinfeng Ye†, and Jiayi Zu† ∗ Universidad Nacional de Educación a Distancia (UNED) † University of Auckland https://www.slideshare.net/mmmcastro
- 2. Overview • The Covid-19 pandemic necessitated a rapid transition to online education, forcing most academic institutions to adopt online assessments as a substitute for traditional, in-person examinations • Many of these online assessments were conducted in an unsupervised setting, with an underlying model that largely relied on the trust that students would maintain academic integrity and adhere to the principles of honest scholarship • Unfortunately, this trust-based approach showed its vulnerabilities, as we observed a significant uptick in incidents of cheating and academic dishonesty across many educational institutions including our own
- 4. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% A B C F 2019 2020
- 5. 0% 10% 20% 30% 40% 50% 60% A B C F 2019 2020 And nearly all students pass Course B example + more time + open Internet - cheating / collaboration
- 6. Aim •We present here our first-hand observations of cheating behaviors (anomalies) and describes a tool (automated) we developed to detect potential cheating in online assessments •We also discuss some preventive measures to curtail the observed cheating behaviours
- 7. Collusion attempts • There were plenty of collusion cases •Two main categories: •Local exam parties •Remote exam parties
- 8. Local Exam Parties A group of students come together in one location to collaboratively solve exam questions Most online assessment platforms record the IP addresses of the users, allowing for the identification of students who share the same IP address. This is a common indicator of a local exam party, as many students sharing a single IP address suggests that they are in close physical proximity • And/or sharing a residence/dormitory • And/or using a VPN 47.52.2.125 ALICLOUD-HK
- 9. Remote Exam Parties Here a student uses multiple IP addresses, which may be located in different geographic regions • And/or substitutes • And/or leader/follower
- 10. 111.40.52.215 China Mobile (CN) 113.5.13.249 China Unicom (CN) 202.36.244.177 University of Auckland (NZ) Here a student uses multiple IP addresses, which may be located in different/concurrent geographic regions • And/or multiple device network (pc/mobile)
- 11. Learning Management System or Digital Assessment Platform Other Resources (e.g., country indicators, maps, etc.) Proxy API access to logs API access Online Application that generates Student Reports and Course Reports
- 12. Check dd/mm/yy --:-- -- 📆 Start time: dd/mm/yy --:-- -- 📆 End time: Please enter an integer Canvas Course ID: Please enter an integer Canvas Student ID: Choose log file Inspera ▼ Select log type: No file chosen Download report(s) Student ▼ Select report type: Canvas as LMS Inspera / CodeRunner apps logs
- 13. 47.52.2.125 ALICLOUD-HK Here a student uses single IP addresses, as part of the campus residences • Should be legitime • Or sharing credentials
- 14. Preventive Measures •Enforce remote proctoring services • R exams / Dividni • Oral / Supervised examinations / Use guards against Chegg Go back to academic integrity •Use assessment designs that minimize the ability to cheat • Most assignment designs do not scale to large classes • Consider individualized assessments •Most of the assessment designs are prone to cheating using generative AI • This underlines the importance of invigilation
- 15. Observations of Cheating Behaviours in Online Examinations and Tools for Mitigation Manuel Castro∗, Sathiamoorthy Manoharan† Ulrich Speidel†, Xinfeng Ye†, and Jiayi Zu† ∗ Universidad Nacional de Educación a Distancia (UNED) † University of Auckland https://www.slideshare.net/mmmcastro
Editor's Notes
- While sharing an IP address with another student is a potential indicator of a local exam party, it doesn't necessarily mean that collusion has occurred. For instance, students in the same WiFi network (e.g. in their shared accommodation) is likely to have the same IP address during their examinations.
- Each student in a remote exam party would solve a subset of questions for all the students in the party. Multiple IP addresses may also be seen when a student hires one or more substitutes to engage in contract cheating However, there are legitimate uses for multiple IP addresses: Use multiple devices. If, for example, a student uses a phone connected to a mobile service provider and a computer connected to a home network during an examination, two IP addresses would be observed. Use of a VPN to access services abroad (e.g., in China)
- Sample student report indicating logging into the examination using two geo-locations. In this case, from the physical university network (which is NOT a VPN exit node) and from two Chinese addresses. Case reported to disciplinary review.
- This is the overall architecture of the analysis tool. Central to the tool is a proxy that acts as the gateway to access student logs using API calls (and an API key). The proxy also serves as the gateway to access other related resources such as IP registration data and location indicators (e.g., world map, country flags).
- This shows the minimalistic UI for generating student and course reports for Canvas LMS. This is the minimalistic UI for uploading Inspera/CodeRunner logs for analysis and subsequent download of student/course reports.
- Multiple students sharing the same IP address. In this case, the address is valid VPN exit node facilitated by the university, and therefore this is legitimate.