Open AccessArtificial Neural Network (ANN) to Predict Mathematics Students’ Performance
Author(s) -
Norpah Mahat,
Nor Idayunie Nording,
Jasmani Bidin,
Sharizal Hasan,
Teoh Yeong Kin
Publication year - 2022
Publication title -
journal of computing research and innovation
Language(s) - English
Resource type - Journals
ISSN - 2600-8793
DOI - 10.24191/jcrinn.v7i1.264
Subject(s) - artificial neural network , gradient descent , function (biology) , artificial intelligence , matlab , computer science , machine learning , correlation coefficient , process (computing) , algorithm , mathematics education , mathematics , evolutionary biology , biology , operating system
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.