Applying Data Mining Techniques for Generating MOOCs Recommendations on the Basis of Learners Online Activity

With the enormous increase in Massive Open Online Courses across different platforms and domains, the course related information is being overloaded. It becomes a very tedious task for the learners to search for the required courses matching their individual goals, knowledge, and interest. MOOCs recommender system plays a vital role by easing this task and providing courses of interest within an efficient time frame. This paper proposes an effective MOOC recommender system with the help of various data mining techniques. It encashes upon the fact that the learners involved in the MOOCs can be easily divided into two categories of active and passive learners based upon their activity logs. It first channelizes the learners into these categories and then provides separate recommendations by applying different data mining approaches. Through this technique, an average accuracy of 92 percent was achieved in case of active learners for the course recommendations.