Detecting and Preventing "Multiple-Account" Cheating in Massive Open Online Courses

Computers & Education(2016)

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摘要
We describe a cheating strategy enabled by the features of massive open online courses (MOOCs) and detectable by virtue of the sophisticated data systems that MOOCs provide. The strategy, Copying Answers using Multiple Existences Online (CAMEO), involves a user who gathers solutions to assessment questions using a “harvester” account and then submits correct answers using a separate “master” account. We use a small-scale experiment to verify CAMEO and estimate a “lower bound” for its prevalence among 1.9 million course participants in 115 MOOCs from two universities. Using conservative thresholds, we estimate CAMEO prevalence at 1237 certificates, accounting for 1.3% of the certificates in the 69 MOOCs with CAMEO users. Among earners of 20 or more certificates, 25% have used the CAMEO strategy. CAMEO users are more likely to be young, male, and international than other MOOC certificate earners. We identify preventive strategies that can decrease CAMEO rates and show evidence of their effectiveness in science courses.
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关键词
Massive Open Online Courses (MOOCs),Cheating detection,Educational certification,Educational Data Mining (EDM),Security,Architecture for educational technology system,Learning communities,Lifelong learning,Pedagogical issues,Teaching/learning strategies
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