Student cluster analysis based on Moodle data and academic performance indicators

摘要

The present work considers the possibility of using Moodle course logs and student performance indicators within the Database Systems course to apply the K-Means clustering algorithm. Clusters of students are identified and explained to partition students with similar study behaviours and performance. Moreover, the understanding of the five groups emerged in cluster analysis allowed us to identify a cluster that contains 86% of students at risk of not completing the course activity. One important aspect that differentiates our study from other similar works is the use of data collected over a long period of time, from 2015 to 2019. The final data set, obtained after preprocessing, contains no less than 185.206 course logs.