Analyzing student performance using evolutionary artificial neural network algorithm

Abstract

Educational Data Mining (EDM) and Learning Systematic (LS) research have appeared as motivating areas of research, which are clari-fying beneficial understanding from educational databases for many purposes such as predicting student's success factor. The ability to predict a student's performance can be beneficial in modern educational systems. This research work aims at developing an evolutionary approach based on genetic algorithm and the artificial neural network. The traditional artificial neural network lacks predicting student performance due to the poor modeling structure and the capability of assigning proper weights to each node under the hidden layer. This problem is overwhelmed with the aid of genetic algorithm optimization approach which produces appropriate fitness function evaluation in each iteration of the learning process. The performances gradually increase the accuracy of the prediction and classification more pre-cisely.