Educational data integration andmachine learning for academic performance prediction
Introduction: This article is the result of the research project “Integration of Educational Data and Machine Learning Techniques for the Prediction of Student Academic Performance”, conducted at Universidad Distrital Francisco José de Caldas between 2021 and 2023.
Problem: When processed with appropriate tools, educational data can be used to predict, prevent, and take action to improve students' academic performance.
Objective: The aim of this study is to predict academic performance in three engineering programs using machine learning techniques. The dataset comprises a total of 7,000 student records.
Methodology: Approximately 325 variables were analyzed in each run. The most influential variables were selected for each academic semester. Feature selection methods revealed standard variables that consistently influence academic performance, regardless of the type of engineering program.
Results: The prediction models were evaluated using supervised learning algorithms (SVC, KNN, Decision Tree, LDA) and ensemble methods, including Bagging techniques (RandomForest, ExtraTreesClassifier), Boosting techniques (AdaBoost, GBM, XGBoost, CatBoost, LightGBM), and Voting techniques (Blending, Stacking).
Conclusion: The proposed model, which uses a super learner algorithm (in one- and two-stage configurations), yielded the highest prediction accuracy for academic performance, followed by Stacking and Blending algorithms. The models achieved average accuracy scores of 85% for training and 75% for testing.
Originality of the Study: This study stands out for its integration of heterogeneous sources of academic, administrative, and socioeconomic data, combined with statistical analysis and advanced machine learning techniques, to generate predictive models tailored to the Colombian educational context.
Limitations: The study identified limitations in the availability and quality of historical data, as well as the absence of certain contextual variables not captured by institutional information systems. These factors may affect the generalizability of the models to other educational contexts.
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As the author of the article, I declare that is an original unpublished work exclusively created by me, that it has not been submitted for simultaneous evaluation by another publication and that there is no impediment of any kind for concession of the rights provided for in this contract.
In this sense, I am committed to await the result of the evaluation by the journal Ingeniería Solidaría before considering its submission to another medium; in case the response by that publication is positive, additionally, I am committed to respond for any action involving claims, plagiarism or any other kind of claim that could be made by third parties.
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