Centroid-based Clustering for Student Models in Computer-based Multiple Language Tutoring

This paper proposes an approach for the initialization and the construction of student models in an intelligent tutoring system that teaches multiple foreign languages. The basic concept for the construction of the initial user models is to assign each new student to a model with similar characteristics. As it is quite easy to understand that a tutoring system has rather little information about its new users, our effort is to provide as much information as possible for each specific user relying on the user’s initial data. To this end, a machine learning algorithm, namely k-means, is responsible for creating clusters relying on the system’s pre-entered past data and as a next step, each new entry is assigned to the nearest centroid.