Reasoning of the student’s performance based on adaptive neuro-fuzzy inference system

Abstract

This research project introduces a systematic approach for the design of a fuzzy inference system (FIS) based on a class of neural networks to assess the students’ performance. Fuzzy systems have reached a recognized success in several applications to solve diverse class of problems. Currently, the combination of fuzzy systems and neural networks is the most successful applications of soft computing techniques with hybrid characteristics and learning capabilities. The developed method uses a fuzzy system augmented by neural networks to enhance some of its characteristics like flexibility, speed, and adaptability, which is called the adaptive neuro-fuzzy inference system (ANFIS). Assessment and reasoning the student performance is not an easy task, especially when it involves many attributes or factors. Moreover, the knowledge of the human experts is acquired to determine the criteria of students’ performance and the decisions about their level of mastery but most of the information is incomplete and vague. To overcome the problem, these projects will carry out the reasoning of the student’s performance based on ANFIS. The method can produce crisp numerical outcomes to predict the student’s performance. The results of the ANFIS approach will be compared to human expert FIS approach.