Applying Data Mining Techniques in MOOC Recommender System for Generating Course Recommendations

Abstract

With the 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 research work 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 bifurcated 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 a significant level of accuracy has been achieved over other basic methods of course recommendations.