A Recommendation System Involving a Hybrid Approach of Student Review and Rating for an Educational Video

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Abstract In face-to-face learning, lecturers directly manage their students and use all their emotional expressions to assess their understanding of courses. Contrastingly, in e-learning, online platforms suffer from the absence of emotional communication. Therefore, this paper addresses educational videos because they are the top closed type of delivered data that can express and clarify details about a subject's meaning. This paper's authors propose an educational video recommendation system based on the hybridization of student historical data. The system will recommend educational videos on learning platforms (the Coursera platform is a case study). We use latent Dirichlet allocation (LDA) for probabilistic topic models. LDA extracts topics from online video content. Simultaneously, we use sentiment analysis of learners' comments on educational videos to make new rating predictions and then hybrid the original user-rating matrix and predicted rating matrix to obtain a better recommendation.

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