Student Modelling on Language Teaching Based on Bayesian Networks

Abstract

Acquiring a reliable student model is the principal task of an Intelligent Tutoring System (ITS). A precisely defined student model is also the key term for the success of ITSs. In this paper, a study on inferring the accurate student model on language learning is offered by utilizing artificial intelligence. In addition to the general structure of an ITS, a probabilistic model for inference using Bayesian Networks is stated in the paper. Bayesian Networks are acyclic directed graphs in which nodes represent random variables and arcs represent direct probabilistic dependences among them. In this study, graphical models and structures are implemented in a general-purpose decision modelling system SMILE and its Windows user interface, GeNIe, developed at the Decision Systems Laboratory. GeNIe user interface is also used in this study to perform Influence diagrams. Influence diagrams represent decision problems and help to choose a decision alternative with the highest expected gain. Toward the end of the study, an ITS student model which is directly associated with a standard proficiency level is aimed to be developed. This model is also complemented with a domain model which incorporates language components such as grammar, vocabulary, and functions of language in different cases. At the end of the study, an evaluation of the model is performed with an experimental study and the results show that the participants worked with offered ITS model gathered close results when compared to those obtained by the participants who worked with a real tutor.