Learning to Cheat: Quantifying Changes in Score Advantage of Unproctored Assessments over Time

Binglin Chen, Sushmita Azad, Max Fowler, Matthew West, Craig Zilles

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Proctoring educational assessments (e.g., quizzes and exams) has a cost, be it in faculty (and/or course staff) time or in money to pay for proctoring services. Previous estimates of the utility of proctoring (generally by estimating the score advantage of taking an exam without proctoring) vary widely and have mostly been implemented using an across subjects experimental designs and sometimes with low statistical power. We investigated the score advantage of unproctored exams versus proctored exams using a within-subjects design for N = 510 students in an on-campus introductory programming course with 5 proctored exams and 4 unproctored exams. We found that students scored 3.32 percentage points higher on questions on unproctored exams than on proctored exams (p < 0.001). More interestingly, however, we discovered that this score advantage on unproctored exams grew steadily as the semester progressed, from around 0 percentage points at the start of semester to around 7 percentage points by the end. As the most obvious explanation for this advantage is cheating, we refer to this behavior as the student population "learning to cheat". The data suggests that both more individuals are cheating and the average benefit of cheating is increasing over the course of the semester. Furthermore, we observed that studying for unproctored exams decreased over the course of the semester while studying for proctored exams stayed constant. Lastly, we estimated the score advantage by question type and found that our long-form programming questions had the highest score advantage on unproctored exams, but there are multiple possible explanations for this finding.

Original languageEnglish (US)
Title of host publicationL@S 2020 - Proceedings of the 7th ACM Conference on Learning @ Scale
PublisherAssociation for Computing Machinery
Pages197-206
Number of pages10
ISBN (Electronic)9781450379519
DOIs
StatePublished - Aug 12 2020
Event7th Annual ACM Conference on Learning at Scale, L@S 2020 - Virtual, Online, United States
Duration: Aug 12 2020Aug 14 2020

Publication series

NameL@S 2020 - Proceedings of the 7th ACM Conference on Learning @ Scale

Conference

Conference7th Annual ACM Conference on Learning at Scale, L@S 2020
CountryUnited States
CityVirtual, Online
Period8/12/208/14/20

Keywords

  • assessment
  • cheating
  • cs1
  • exams
  • proctoring
  • stem

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications

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