Ubiquitous Learning Analytics Using Learning Logs

To the new international students who learn Japanese out-class in Japan, it is too hard to find different suitable ways for different students that have different learning characteristics. This paper considers to solve this problem, which is how to help new international students who learn Japanese for out-class learning according student’s own learning frequency. This paper uses learning frequency as the point to understand students’ learning behavior characteristics so that distinguish among different learning characteristics. The proposal algorithm in this paper helps international students to find similar students who have the similar information background and similar learning characteristics, and then recommends the new student suitable learning contents. To achieve the goal, this paper uses learning analytics method based on SCROLL system. This paper uses kmeans clustering to build student learning frequency model, and predict the relationship between user information and frequency model by classification. After finding the similar student for new student, the system will recommend learning content what the similar have learned to the new student. This paper compares the difference among Bayesian Network, C4.5 and Neural Network in our program.