Clustering Students for Group-Based Learning in Foreign Language Learning

Big data make it possible to mine learning information for insights regarding student performance in foreign language learning (FLL). Group-based learning is a usual method to improve FLL, whose effectiveness is greatly influenced by student groups. The general grouping method is to divide students into groups by their teacher manually, which is not timely or accurate. To overcome the shortcomings of manual methods, this paper proposes an automatic grouping method based on clustering technologies. First, the student profile is built to model the student's knowledge level, which can be updated based on the results of examinations automatically. Then, to meet the different teaching goals, two student clustering methods are proposed: similarity student clustering and complementation student clustering. At last, the proposed methods are evaluated by comparing the students of clustered groups with those of the manual groups in the learning effectiveness. The experimental results show that the proposed methods are flexible, comprehensive, and timely compared with manual group methods.