Clickstream-based outcome prediction in short video MOOCs

Gábor Korösi, Peter Esztelecki, Richárd Farkas, Krisztina Tóth

Abstract: In this paper, we present a data mining approach for analysing students' clickstream data logged by an e-Iearning platform and we propose a machine learning procedure to predict course completion of students. For this, we used data from a short MOOc course which was motivated by the teachers of elementary schools. We show that machine learning approaches can accurately predict the course outcome based on clickstream data and also highlight patterns in data which provide useful insights to MOOc developers.