STUDENT MODELLING ON LANGUAGE TEACHING BASED ON BAYESIAN NETWORKS

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