Artificial Neural Network (ANN) to Predict Mathematics Students' Performance

Norpah Mahat; Nor Idayunie Nording; Jasmani Bidin; Suzanawati Abu Hasan; Teoh Yeong Kin

doi:10.24191/jcrinn.v7i1.264

Artificial Neural Network (ANN) to Predict Mathematics Students' Performance

Authors

Norpah Mahat Universiti Teknologi MARA, Perlis Branch, Arau Campus
Nor Idayunie Nording Universiti Teknologi MARA, Perlis Branch, Arau Campus
Jasmani Bidin Universiti Teknologi MARA, Perlis Branch, Arau Campus
Suzanawati Abu Hasan Universiti Teknologi MARA, Perlis Branch, Arau Campus
Teoh Yeong Kin Universiti Teknologi MARA, Perlis Branch, Arau Campus

DOI:

https://doi.org/10.24191/jcrinn.v7i1.264

Keywords:

Artificial neural network, ANN, Marquardt, Levenberg , prediction model

Abstract

Predicting students' academic performance is very essential to produce high-quality students. The main goal is to continuously help students to increase their ability in the learning process and to help educators as well in improving their teaching skills. Therefore, this study was conducted to predict mathematics students' performance using Artificial Neural Network (ANN). The secondary data from 382 mathematics students from UCI Machine Learning Repository Data Sets used to train the neural networks. The neural network model built using nntool. Two inputs are used which are the first and the second period grade while one target output is used which is the final grade. This study also aims to identify which training function is the best among three Feed-Forward Neural Networks known as Network1, Network2 and Network3. Three types of training functions have been selected in this study, which are Levenberg-Marquardt (TRAINLM), Gradient descent with momentum (TRAINGDM) and Gradient descent with adaptive learning rate (TRAINGDA). Each training function will be compared based on Performance value, correlation coefficient, gradient and epoch. MATLAB R2020a was used for data processing. The results show that the TRAINLM function is the most suitable function in predicting mathematics students' performance because it has a higher correlation coefficient and a lower Performance value.

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