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

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.

Students Final Academic Score Prediction Using Boosting Regression Algorithms

“…In addition, a few researchers only take a look for classic regressions and classifiers on predicting student performance. For example, research conducted by Abdelrahman A (2022) [35] aims to predict good/bad the final exam grade. The proposed model was developed by Abdelrahman A, who evaluated a range of popular classification and regression algorithms using data from a data structures and algorithms course (CS2) offered at a large public research university.…”

“…Although some studies above have made valuable contributions related to the prediction and classification of the Portuguese student dataset, there is a significant research gap. These previous studies only considered only a few prediction regressions, especially only developed with few classic regressions, without adding such modern regressions like gradient boosting regressions [32], [35]. Therefore, this study aims to address this gap by utilizing 3 gradient boosting methods such as XGBoost Regressor, LGBM, and CatBoost Regressor for predicting students' final academic scores (G3) [42].…”