Detecting contract cheating using learning analytics

Kelly Trezise, Tracii Ryan, Paula de Barba, Gregor Kennedy

Research output: Contribution to journalArticleResearchpeer-review

17 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)90-104
Number of pages15
JournalJournal of Learning Analytics
Volume6
Issue number3
DOIs
Publication statusPublished - 2019
Externally publishedYes

Keywords

  • Contract cheating
  • Keystroke logs
  • Latent profile analysis
  • Plagiarism
  • Writing process

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