Mining relationships among user clusters in Facebook for language learning

This paper describes the mining of relationships among user clusters in Facebook for tutoring languages. In this study, we have visualized the Facebook user characteristics used in classification procedure. We applied K-means clustering algorithm to determine the groups of users with the same learning styles and capabilities. The aforementioned algorithm groups them by taking as input, to initialize the process, several fundamental user characteristics. Our study exploits the fact that tutoring systems have a large number of users and we use a machine learning reasoning mechanism, which is based on recognized similarities between them. The overall goal of this data mining process is to extract information from the user data set and transform it into an understandable structure for further use. Future plans include deeper study on the relationship between the different Facebook characteristics and clarifying which characteristic has the strongest effect on the clustering procedure.