An adaptive and intelligent e-learning environment using fuzzy logic

A major issue of great concern in student modeling is the inherent uncertainty that exist due to lack of sufficient information with high degree of certainty to build this model. The proposed system presents a novel approach which is a web-based education system that performs individualized instruction on the domain of programming languages is presented. This approach is fully implemented and evaluated in an educational application module, called Fuzzy Knowledge and Preference Based E earning (Fuz KPBE). This also tracks the events and preferences of the users before finding the data. In particular, Fuz KPBE Performs user modeling by dynamically identifying and updating a student's knowledge level of all the concepts of the domain knowledge. The operation of Fuz KPBE is based on Fuzzy clustering by Local Approximation of Memberships (FLAME ) that are used to represent the dependences among the domain concepts. Fuz KPBE uses fuzzy sets to represent a student's knowledge level as a subset of the domain knowledge. Thus, it combines fuzzy theory with the overlay model. Moreover, it employs a novel inference mechanism that dynamically updates user stereotypes using fuzzy sets. It should be noted that the overlay model and stereotypes constitute two widely used methods for user modeling.