With the fast development of MOOC in recent years, one MOOC platform has more than millions of learners and thousands of courses. Personality course recommendation is necessary to help people discover potentially interested courses by analyzing user behaviors. Collaborative Filtering (CF) has been shown to be effective on recommending items according to users in same interests on same items. In this paper, considering the effectiveness and efficiency of CF, we proposed a method called Multi-Layer Bucketing Recommendation (MLBR) to recommend courses on MOOC. MLBR changes learner vectors into same length dimension and scatters them into buckets which contain similar learners with more courses in common. At the same time, MLBR reduces the time cost of online, offline and update computation in CF recommendation. Furthermore, we extend MLBR with map-reduce technique to improve the efficiency. Extensive experiments on real-world MOOC datasets demonstrate the effectiveness and efficiency of the proposed model. ß 2017 Wiley Periodicals, Inc. Comput Appl Eng Educ 25:120-128, 2017; View this article online at wileyonlinelibrary.com/journal/cae;

Massive Open Online Course (MOOC) has developed rapidly in recent years. However, the low satisfaction and the feelings of loneliness tend to cause more dropouts. A solution called Adaptive Recommendation for MOOC (ARM) is proposed aiming at the problem. Traditional MOOC recommendations are usually on the feature of interest. Among the recorded MOOC data, new recommendation features are selected for better balance on satisfaction. ARM trades off features adaptively according to the learner's requirement of satisfaction. Collaborative Filtering provides explicit information of similar learners and supports Collaborative Learning for less loneliness. ARM creatively combines Collaborative Filtering and time series to improve the recommendation accuracy. Specifically, Hawkes point process is improved to model the motivate and demotivate effect of score for future learning. Experiments with real‐world data show the accuracy of the ARM in recommendations and improvements in the dropout rate.

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MOOC becomes a new learning mode in only two years. Many MOOC providers emerge and expand rapidly. Some is well known such as Coursera, Udacity and edX. Related information about them changes everyday. We do some research on the newest information about MOOC providers. We introduce few relatively new MOOC providers. At the same time we do some comparision and anylysis on some typical MOOC providers. We also summarize course data from them for anonymous user that may be used for further educational research. Based on those work we notice all MOOC providers have some common attributes , while they have found their own way to find users they would like to attract.