A Personalized Learning Path Recommender System with LINE Bot in MOOCs Based on LSTM

MOOCs has a great impact on nowadays educational strategies. MOOCs enable global learners to learn without time and space constraints, allowing distinct learning characteristics when participating in online courses. Overwhelmed by complicated learning resources, a problem named “information overload” was widely discussed in online education. AI-based Recommender System, which is recognized as the powerful solution to improve resource acquisition via customized supply, has been regarded as an assistant in online learning by giving personalized learning strategies. In this paper, a Personalized Learning Path Recommender System with LINE Bot is proposed to meet personal preferences on path of learning. A LSTM model is built to consider video-watching preference features, clusters of students and learning paths to recommend personal learning path suitable for each student. Related recommendation contents and prediction results will be received by users through in-time LINE massages, achieving the goal of making in-time and active recommendations. From the evaluation part, F1-score of the proposed Learning Path Prediction Model is 0.8, indicating this model has a certain degree of accuracy. On the other hand, the proposed system is used in two courses of NTHU Cloud to give personalized learning path guidance. The experimental results demonstrate that learning path recommendations will help students have stronger learning willingness to keep learning, and help plan proper study steps to fulfill their own learning needs. On the other hand, this system provides another way except examinations to make judgements about one's learning status, and most learners agree that this kind of recommendations is helpful to review unfamiliar concepts and catch up with others.