Predicción del bajo rendimiento académico en programación de computadoras mediante técnicas de minería de datos educativa en muestras reducidas
Keywords:
Ensemble model, Random forest, XGBoost, Poor academic performance, Educational data mining.Abstract
The objective of this research was to demonstrate the feasibility of a predictive model for poor academic performance applicable in contexts with limited data (n=94 first-terms students), integrating psychoeducational variables (personality traits and school climate) and specialized techniques (SMOTE + ReliefF + assemblies). Using Educational Data Mining (EDM) techniques, this study is presented as quantitative and non-experimental applying two Likert-scale instruments: one measuring motivation and another evaluating personality traits based on the Big Five model (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism). The dataset was processed using SMOTE (to balance minority classes) and ReliefF (for feature selection), reducing from 45 variables to 16 key predictors. Four base algorithms (Random Forest, XGBoost, Gradient Boosting, Extra Trees) and a voting ensemble classifier were evaluated. Cross-validation (k=10) and metrics including accuracy, precision, sensitivity, F-measure, and AUC were used to measure performance. However, the sensitivity (50%) revealed difficulties in detecting positive cases, likely due to class imbalance. These results demonstrate the effectiveness of the ensemble method techniques in predicting poor academic under performance, they provide educators with useful information to design early interventions and improve academic outcomes. The ensemble model achieved the highest accuracy (87%), precision (71%), and AUC (72%), outperforming other classifiers.
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