Measuring Performance Quality of Art2 & Fuzzy-Art2 Algorithms in Student Knowledge Modelling

Abstract

This paper handle the basic structure of the student model used within learning management systems to generate intelligent and adaptive courses using some neural network algorithms, The paper also focuses on how to modelling cognitive cases for Elneelain university students for each goal or concept of education within any subject using the Art2 and Fuzzy-art2 in student records (input vectors) and to classify student records into six varieties: very weak, weak, medium, good, very good and excellent. The paper also focuses on measuring the performance quality of each algorithm using the F-measure standard to choose the most appropriate time and accuracy algorithm to be used consistently within learning management systems.