Extending Bayesian Personalized Ranking with Survival Analysis for MOOC Recommendation

Abstract

Massive Open Online Courses (MOOCs) have recently attracted students and professionals as complementary tools to academic education. Despite the number of advantages MOOCs provide, such as openness and flexibility regarding learning pace, such courses are characterized by a consistently higher dropout rate than conventional classrooms. A crucial factor that influences dropout is the choice of the appropriate course, hence the need for effective course recommendations. A course recommendation system (RS) that uses dropout information can mitigate course withdrawal and user dissatisfaction. In this paper, an extension of Bayesian Personalized Ranking, which is a learning-to-rank RS, is proposed that uses the pseudo-labels extracted by survival analysis based on dropout information to recommend courses in the context of MOOCs. The proposed approach performs the best compared to six competing RSs on three MOOCs datasets.