Predictive analysis and modeling of academic performance using machine learning techniques in a secondary education institution
DOI:
https://doi.org/10.51252/rcsi.v6i1.1212Keywords:
machine learning, data science, secondary education, pythonAbstract
Academic performance is a key indicator for evaluating educational quality and identifying areas for improvement in teaching and learning processes. This study analyzes a dataset of first-year lower secondary students from an educational institution in the province of Salta, Argentina, with the aim of identifying variables that influence student performance and supporting decision-making to mitigate low academic achievement. Following the CRISP-DM methodology, an exploratory analysis was conducted to identify relevant patterns in grades, unsupervised learning models were applied to detect student profiles, and supervised models were used to predict year completion based on second-term grades. The best-performing model achieved an F1-score of 0.80 for the minority class and an overall accuracy of 89%. The results enable early identification of students at academic risk and the segmentation of student profiles, providing valuable insights for more effective pedagogical interventions.
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Copyright (c) 2026 Alejandro Miguel Zalasar, Ramón Aramayo, Cristian Alejandro Martínez
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