Time Series Clustering of Moodle Activity Data

Modern computer systems generate large volumes of log data as a matter of course and the analysis of this log data is seen as one of the most promising opportunities in big data analytics. Moodle is a Virtual Learning Environment (VLEs) used extensively in third level education that captures a significant amount of log data on student activity. In this paper we present an analysis of Moodle data that reveals interesting differences in student work patterns. We demonstrate that, by clustering activity profiles represented as time series using Dynamic Time Warping, we can uncover meaningful clusters of students exhibiting similar behaviours. We use these clusters to identify distinct activity patterns among students, such as Procrastinators, Strugglers, and Experts. We see educators as the potential users of a tool that might result from this research and our preliminary analysis does identify scenarios where interventions should be made to help struggling students.