Student modeling using fuzzy logic and neural networks

In this thesis a neural network-based fuzzy modeling approach to assess student learning characteristic and update the student model in Intelligent Learning Environments is proposed. The neural network-based fuzzy diagnostic model is a general diagnostic model which can be used to implement the diagnostic process in any learning environment according to designers’ and teachers' suggestions. Fuzzy logic is used to provide a linguistic description of students' behavior and learning characteristics, as they have been elicited from teachers, and to handle the inherent uncertainty associated with teachers’ subjective assessments. Neural networks are used to add learning and generalization abilities to the fuzzy model by encoding teachers' experience through supervised neural-network learning. The model has been successfully implemented, trained and tested in the learning environment "Vectors in Physics and Mathematics" by using the recommendations of a group of five experienced teachers.