Student’s Academic Performance Prediction Using Factor Analysis Based Neural Network

This study focused on the statistical technique using the neural network, hybrid models and factor analysis on constructing the new factors affecting students learning styles of the survey done among university students in predicting academic performance. The data were collected using survey questionnaires and students’ academic records. The methodologies used were descriptive statistics, factor analysis, neural network and hybrid models technique using the following Learning algorithms; Levenberg-Marquardt (LM), Bayesian Regularization (BR), BFGS Quasi-Newton (BFG), Scaled Conjugate Gradient (SCG), Gradient Descent (GD) in artificial neural network model while for the second Hybrid model only the best two algorithms where use; Levenberg-Marquardt (LM), Bayesian Regularization (BR). The results showed ten new factors were successfully constructed using factor analysis and the proposed hybrid models show that though it took longer time and number of epochs to train the hybrid models by Bayesian Regularization Algorithms, and it gives more accurate predictions than both the Levenberg-Marquadrt, Scaled Conjugate Gradient, Gradient Descent and BFGS Quasi-Newton (BFG) Algorithms. In a nutshell, the finding indicates that Bayesian Regularization is the best learning algorithms in both Neural Network and Hybrid models for predicting students’ academic performance.