MOOCs Recommender Based on User Preference, Learning Styles and Forum Activity

Abstract

With the development of MOOCs (Massive Open Online Courses) as a major source of e-learning materials, the number of MOOCs available today has become dauntingly high. Furthermore, MOOCs are produced in many different video production styles and these styles play an important role in helping the consumer stay engaged and interested in the course throughout. However, due to the sheer number of MOOCs available today, it is becoming increasing difficult to find the MOOCs that suits your personal preferences and the learning style. This paper describes how thousands of MOOCs that belong to different styles are identified efficiently while each consumer's preferences are identified to provide personalized MOOC recommendations. Furthermore, the paper describes how forums can be analyzed to identify how consumers feel about MOOCs that they followed, which is a crucial metric in recommending MOOCs to consumers.