Detecting Academic Misconduct Using Learning Analytics

Main Article Content

Kelly Trezise
Tracii Ryan
Paula de Barba
Gregor Kennedy

Abstract

Keystroke logging and clickstream data, both emergent areas of study in the field of learning analytics, present promising alternative methods of detecting and preventing contract cheating. The current study examines whether analysis of keystroke and clickstream data can detect when a student is creating their own authentic writing or transcribing from another source. Participants were 62 university students (47 women, 15 men) who completed three writing tasks under experimental conditions: free writing, general transcription, and self-transcription. Analyses revealed that while completing the free-writing task, participants typed in shorter bursts with longer pauses and typed more slowly with more revisions compared to the two transcription tasks. Model-based clustering was able to accurately distinguish the free-writing task from the two transcription tasks based on patterns of bursts and writing speed. Overall, these results suggest that keystroke and clickstream analysis may be able to distinguish between a student writing an authentic piece of work and one transcribing a completed work. These findings signal significant implications for the detection of contract cheating.

Article Details

How to Cite
Trezise, K., Ryan, T., de Barba, P., & Kennedy, G. (2019). Detecting Academic Misconduct Using Learning Analytics. Journal of Learning Analytics, 6(3), 90–104. https://doi.org/10.18608/jla.2019.63.11
Section
Research Papers

References

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