Towards a General Purpose Anomaly Detection Method to Identify Cheaters in Massive Open Online Courses

We propose a general-purpose method for detecting cheating in Massive Open Online Courses (MOOCs) using an Anomaly Detection technique. Using features that are based on measures of aberrant behavior, we show that a classifier that is trained on data of one type of cheating (Copying Using Multiple Accounts) can detect users who perform another type of cheating (unauthorized collaboration). The study exploits the fact that we have dedicated algorithms for detecting these two methods of cheating, which are used as reference models. The contribution of this paper is twofold. First, we demonstrate that a detection method that is based on anomaly detection, which is trained on a known set of cheaters, can generalize to detect cheaters who use other methods. Second, we propose a new time-based person-fit aberrant behavior statistic.