Identifying the Determinants of Academic Success: A Machine Learning Approach in Spanish Higher Education

• Published in: Systems (Basel) Vol. 12; no. 10; p. 425
• Main Authors: Sánchez-Sánchez, Ana María, Mello-Román, Jorge Daniel, Segura, Marina, Hernández, Adolfo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2024
• Subjects: Academic achievement; academic performance; Algorithms; Analysis; At risk students; Colleges & universities; Data mining; Distance learning; Education; Education, Higher; educational data mining; Higher education; Impact analysis; Learning strategies; Lifelong learning; Literature reviews; Machine learning; Online instruction; Participation; Performance prediction; random forest; Random variables; Self-efficacy; Social factors; Student retention; Students; Success; Support vector machines; Sustainable development; Teachers; Variables; XGBoost; Spain
• ISSN: 2079-8954; 2079-8954
• DOI: 10.3390/systems12100425

Academic performance plays a key role in assessing the quality and equity of a country’s educational system. Studying the aspects or factors that influence university academic performance is an important research opportunity. This article synthesizes research that employs machine learning techniques to identify the determinants of academic performance in first-year university students. A total of 8700 records from the Complutense University of Madrid corresponding to all incoming students in the academic year 2022–2023 have been analyzed, for which information was available on 28 variables related to university access, academic performance corresponding to the first year, and socioeconomic characteristics. The methodology included feature selection using Random Forest and Extreme Gradient Boosting (XGBoost) to identify the main predictors of academic performance and avoid overfitting in the models, followed by analysis with four different machine learning techniques: Linear Regression, Support Vector Regression, Random Forest, and XGBoost. The models showed similar predictive performance, also highlighting the coincidence in the predictors of academic performance both at the end of the first semester and at the end of the first academic year. Our analysis detects the influence of variables that had not appeared in the literature before, the admission option and the number of enrolled credits. This study contributes to understanding the factors that impact academic performance, providing key information for implementing educational policies aimed at achieving excellence in university education. This includes, for example, peer tutoring and mentoring where high- and low-performing students could participate.