Analysis of factors affecting the academic performance of university students using machine learning.

ABSTRACT: Understanding the determinants of university students' academic performance has become a strategic priority for higher education institutions, especially in contexts marked by social, economic, and academic diversity. However, performance assessment remains a challenge due to the complexity of the educational process and the nonlinear nature of learning behaviors. A machine learning-based prediction model was developed using primary data from 386 university students. The performance of nine educational data mining (EDM) algorithms, including XGBoost, Random Forest, artificial neural networks (ANNs), Support Vector Machines, Decision Trees, Naive Bayes, Logistic Regression, AdaBoost, and K-Nearest Neighbors (KNN), was evaluated using demographic, socioeconomic, academic, social and family,

SUBMITTER: Marin YR 

PROVIDER: S-EPMC12711901 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature