Predictive teaching and learning

Abstract

In this paper, we present a study about students’ behavior based on activity logs in Moodle (an online Learning Management System LMS) analyzing three characteristics: online time (separated by its location), tasks delivered and support material views. We relate these three characteristics with the students’ performance (i.e. success, fail and dropout) and providing a generalization of four students’ groups (based on their behavior on the LMS). After analyzing these characteristics, we evaluate the correlation between each characteristic and the individual student performance, identifying a promising feature to enrich predictive algorithms. Finally, we generated a Naïve Bayes model to predict if the student will succeed, fail or dropout. To evaluate the prediction, we compared the models generated with only the performance data and the models with the enriched data, according with the previously analyzed features. The results shows that the enriched data model are more accurate and may help the teacher to identify “at risk” students.