Using Visualization Algorithms for Discovering Patterns in Groups of Users for Tutoring Multiple Languages through Social Networking

Abstract

Social networks are addressed to a very large and heterogeneous audience of people. When trying to incorporate an intelligent language learning system in social networks, a problem of user diversity emerges and thus, user clustering based on their characteristics is necessary. In view of this compelling need, this paper concerns the pattern discovery of user clusters in social networks. In this research, we have modeled the Facebook user characteristics that determine the clustering process. An unsupervised clustering algorithm was used so that coherent groups of users with the same learning styles and capabilities are generated. This algorithm clusters users by taking as input their several fundamental characteristics, such as their age, educational level, number of languages spoken and computer knowledge level. The general objective of this data mining process is to extract important information and to gain knowledge from the user data set and transform it into a manageable and intelligible structure with a view to ameliorating the learning process. These experimental results show that the Facebook user characteristics, which were chosen at the clustering process, seem to be significant determinants for the clusters and the whole learning experience of each user.