Student Performance Prediction Using Machine Learning Techniques

Abstract

With the emergence of the covid 19 pandemic, E-learning usage was the only way to solve the problem of study interruption in educational institutions and universities. Therefore, this field has garnered significant attention in recent times. In this paper, we used ten machine-learning algorithms (Logistic Regression, Decision Tree, Random Forest, SGD Classifier, Multinomial NB, K-Neighbors Classifier, Ridge Classifier, Nearest Centroid, Complement NB and Bernoulli NB) to build a prediction system based on artificial intelligence techniques to predict the difficulties students face in using the e-learning management system, and support related decision-making. Which, in turn, contributes to supporting the sustainable development of technology at the university. From the results obtained, we found the important factors that affect the use of E-learning to solve students' learning difficulties by using LMS.