Intelligent student profiling with fuzzy models
Dongming Xu, Huaiqing Wang, Kaile Su · 2003
Traditional Web-based educational systems still have several shortcomings when comparing with real-life classroom teaching, such as lack of contextual and adaptive support, lack of flexible support of the presentation and feedback, lack of the collaborative support between students and systems. Based on educational theory, personalization increases learning motivation, which can increase the learning effectiveness. A fuzzy epistemic logic has been built to present the student's knowledge state, while the course content is modeled by the concept of context. By applying such fuzzy epistemic logic, the content model, the student model, and the learning plan have been defined formally. A multi-agent based student profiling system has been presented. Our profiling system stores the learning activities and interaction history of each individual student into the student profile database. Such profiling data is abstracted into a student model. Based on the student model and the content model, dynamic learning plans for individual students are made. Students get personalized learning materials, personalized quizzes, and personalized advice. In order to understand the students' perception of our prototype system and to evaluate the students' learning effectiveness, a field survey has been conducted. The results from the survey indicate that our prototype system makes great improvement on personalization of learning and achieves learning effectiveness.