Application of Machine Learning to Predict and Explain University Academic Performance
DOI:
https://doi.org/10.5281/zenodo.16121350Keywords:
Affective Factors, Artificial Intelligence, Self-control, Academic Performance, University Students, Academic FactorsAbstract
In the educational field, academic performance represents the outcomes of evaluation processes and is related to students’ learning achievements. Early identification of the factors that influence performance allows for timely interventions to prevent course repetition and student dropout. In this regard, the objective of this study was to apply machine learning models to predict and explain academic performance, with a particular focus on students with a history of failing at least one course. A quantitative approach was used, with a non-experimental, ex post facto design, based on a population of 12,211 university students. Data were collected through a 32-item questionnaire covering sociodemographic, socioeconomic, emotional, institutional-academic, self-efficacy, and self-control aspects, linked to the student enrollment system, as well as an institutional database with seven academic variables. Three supervised classification algorithms were trained: Random Forest, XGBoost, and CatBoost. In addition, the SHAP method was used to interpret the model’s outputs. Data processing and analysis were conducted using Python in the Google Colab environment. CatBoost showed the best performance, achieving a 70% recall for the “failed” class. The most influential indicators were faculty, academic program, academic level or cycle, emotional state, teacher support, and previous academic performance. It is concluded that academic failure is influenced primarily by institutional-academic variables, followed by emotional, sociodemographic, and socioeconomic factors. The value of interpretable machine learning (SHAP) is highlighted as a tool to support educational decision-making.
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