Prediction of Learning Success Via Rate of Events in Social Networks for Education


Earlier studies have shown the power of online learning systems data to develop prediction methods that try to identify successful students patterns of accomplishment and engagement to allow timely pedagogical interventions. Our learning platform, SocialWire, collects a detailed record of the students’ activity so, in this paper, we compare and combine the power of different classifiers for success/failure learning prediction, using as inputs some of the features that have measurable correlation with the students’ performance. The main conclusion is that, for the courses in study, it is not the type of event/activity initiated by the student what best predicts his/her final grade, but the pace of the events he/she was engaged in.

Computer Applications in Engineering Educaction, vol. 26, no. 6, pp. 2047–2057, Nov. 2018.