Abstract
Higher education regulatory authorities, institutions, and students all value the ability to predict students' performance. Several colleges and universities use student data analytics to predict student performance. Several endeavors have been conducted to classify student results using well-known algorithms to attain the required accuracy. This article uses an artificial neural network (ANN) to examine and predict the academic characteristics and performance of students based on certain criteria such as prior academic records, family background, and attitudinal information, among others, so that educators can provide solutions in the event of high-risk students failing. Predicting student performance in school has been made possible using ANN, a machine learning model that has been shown to be dependable and effective for a wide range of functions and applications. When compared to other techniques, the results reveal a higher prediction accuracy of 89.72%. It is vital to compare the predictive outcomes with previous solutions in order to examine their fairness, validity, and reliability. When compared to other algorithms on a given dataset, we found that ANN predicts better due to model complexity control and reduction, as well as successive adjustments achieving the highest accuracy, allowing an ANN to produce target output that is more precisely similar to the actual output.