Monitoring students actions and using teachers expertise in implementing and evaluating the neural network-based fuzzy diagnostic model
In this paper, the implementation of a neural network-based fuzzy modeling approach to assess aspects of students learning style in the discovery learning environment Vectors in Physics and Mathematics is presented. 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 neural network-based fuzzy diagnostic model is a general diagnostic model which is implemented in an Intelligent Learning Environment by eliciting teachers expertise regarding students characteristics based on real students observation and on data being collected from students interaction. 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. The performance of our model in real classroom conditions has been evaluated during an experiment with an experienced Physics teacher and 49 students of secondary school attending Physics lessons.
neural networks learning styles fuzzy logic intelligent learning environments student model