Massive open online course recommendation system based on a reinforcement learning algorithm

Abstract

AbstractMassive open online courses (MOOCs) are open online courses designed on the basis of the teaching progress. Videos and learning exercises are used as learning materials in these courses, which are open to numerous users. However, determining the prerequisite knowledge and learning progress of learners is difficult. On the basis of learners’ online learning trajectory, we designed a set of practice questions for a recommendation system for MOOCs, provided suitable practice questions to students through the LINE chatbot (a type of social media software), and used mobile devices to encourage participation in MOOCs. Reinforcement learning, which involves reward function design and iterative solution improvement, was used to set task goals, including those related to course learning and practice question difficulty. The proposed system encouraged certain learning behaviors among students. Students who used the system exhibited an exercise completion rate of 89.97%, which was higher than that of students who did not use the system (47.23%). The system also increased the students’ overall learning effectiveness. Students who used and did not use the proposed system exhibited average midterm scores of 64.73 and 58.21, respectively. We also collected 227 online questionnaires from students. The results of the questionnaires indicated that 90% of the students were satisfied with the system and hoped to continue using it.