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

TL;DR

This paper introduces a data-driven method to detect and prevent a specific cheating strategy, CAMEO, in MOOCs, revealing its prevalence and characteristics among learners and proposing effective countermeasures.

Contribution

The study develops a novel detection approach for CAMEO cheating in MOOCs and demonstrates its effectiveness and preventive strategies across multiple courses.

Findings

CAMEO prevalence estimated at 1.3% of certificates

25% of high-achieving earners used CAMEO

Preventive strategies reduce CAMEO rates in science courses

Abstract

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 "clickstream" learner data to detect CAMEO use among 1.9 million course participants in 115 MOOCs from two universities. Using conservative thresholds, we estimate CAMEO prevalence at 1,237 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.