Evaluation of Student's Performance and Learning Efficiency Based on ANFIS

Abstract

This work focuses on a systematic approach in assessing and reasoning the student’s performance and efficiency level in Programming Technique course. There are four criteria required to indicate the student’s performance and efficiency level which are scores earned, time spent, number of attempts and help needed. A fuzzy rule base model that has been proposed in previous work is found to be insufficient in deciding all possible conditions. To deal with this problem, this work focuses on the Adaptive Neuro-Fuzzy Inference System (ANFIS) approach in determining the possible conditions in order to form a fuzzy rule based system of a student model. The back- propagation is utilized as the learning mechanism for the neural network to solve the incompleteness in the decision made by human experts. By training the neural network with 18 human decisions that are certain, the neural network has successfully derived other decisions to form a complete fuzzy rule base and able to adjust its parameter by learning mechanism. However, some of the decisions are found illogically classified.