Optimizing programming language learning: A data analytics-based approach for Systems Engineering students at Los Libertadores University Foundation
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Abstract
The research proposes a solid methodology for constructing predictive models of academic performance in Systems Engineering students, based on detailed analysis of academic data, especially in programming and intermediate programming. By utilizing various metrics, it seeks to enhance educational analytics and the prediction of performance in specialized areas. Students from the first and second semesters of the last five years will be analyzed, meticulously collecting data and employing three machine learning techniques to construct predictive models with an accuracy close to 65 %.
Thus, the Naïve Bayes-based model stands out for identifying students prone to repeating and dropping out, examining the most relevant characteristics for predicting academic performance. This approach aims not only to improve predictive capacity in Systems Engineering but also to be replicable in other courses and programs.
Consequently, the methodology will contribute to improving learning processes and generating intervention strategies for students at risk of repetition and dropout, aligning with the research mission of catalyzing a positive impact on educational quality and student well-being, acting as an agent of interdisciplinary transformation.
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