Learning Behavior Analysis and Prediction Based on MOOC Data

With the booming of MOOC (massive open online course) in the past two years, educational data analysis has become a promising research field where the quality of teaching and learning can be and is being quantified to improve the educational effectiveness and even to promote the modern higher education. In the autumn of 2013, Peking University released its first six courses on the Coursera platform. Through mining and analyzing the massive data of learning behavior of over 80000 participants from the courses, this paper endeavors to manifest more than one side of learning activity in MOOC. Meanwhile, according to the characteristic of learning behavior in Chinese MOOC, learners are classified into several groups and then the relationship between their learning behavior and performance is thoroughly studied. Based on the above work, we find out that learners performance, regarding whether heshe could get certificated eventually, can be predicted by looking into several features of their learning behavior. Experiment results indicate that these features can be trained to effectively estimate whether a learner is probably to complete the course successfully. Besides, this method has the potential to partially evaluate the quality of both teaching and learning in practice.