Study Partners Recommendation for xMOOCs Learners

• Published in: Computational Intelligence and Neuroscience Vol. 2015; no. 2015; pp. 1336 - 1345
• Main Authors: Xu, Bin, Yang, Dan
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Limiteds 01.01.2015; Hindawi Publishing Corporation; John Wiley & Sons, Inc; Hindawi Limited
• Subjects: Algorithms; Behavior - physiology; Classification; Collaboration; Computer assisted instruction; Distance education; Education, Higher; Humans; Internet; Learning - physiology; Massive open online courses; Messages; Recommender systems; Social networks; Social Support; Students; Tasks; Teachers; Teaching; China
• ISSN: 1687-5265; 1687-5273
• DOI: 10.1155/2015/832093

Massive open online courses (MOOCs) provide an opportunity for people to access free courses offered by top universities in the world and therefore attracted great attention and engagement from college teachers and students. However, with contrast to large scale enrollment, the completion rate of these courses is really low. One of the reasons for students to quit learning process is problems which they face that could not be solved by discussing them with classmates. In order to keep them staying in the course, thereby further improving the completion rate, we address the task of study partner recommendation for students based on both content information and social network information. By analyzing the content of messages posted by learners in course discussion forum, we investigated the learners’ behavior features to classify the learners into three groups. Then we proposed a topic model to measure learners’ course knowledge awareness. Finally, a social network was constructed based on their activities in the course forum, and the relationship in the network was then employed to recommend study partners for target learner combined with their behavior features and course knowledge awareness. The experiment results show that our method achieves better performance than recommending method only based on content information.