A HYBRID MODEL BASED ON MACHINE LEARNING AND GENERATIVE AI FOR PREDICTING ACADEMIC PERFORMANCE FROM LEARNING TRACES
Abstract
The analysis of students’ learning traces in digital environments enables a better understanding of the factors influencing their academic performance. This paper proposes a hybrid predictive model combining Machine Learning (ML), Deep Learning (DL), and Generative Artificial Intelligence (GenAI) to predict students’ academic performance (GPA) by leveraging traces from six dimensions: demographic, cognitive, social, emotional, contextual, and normative. First, the Random Forest algorithm is used to select the most relevant features. Then, a combined model based on MLP, LSTM, and XGBoost is trained to optimize prediction accuracy. Generative AI (DeepSeek) is integrated to enrich contextual data and provide personalized recommendations. Model decisions are interpreted using SHAP, allowing for explainability of the predictions. The model is evaluated on a real dataset of 1,002,393 records, built from learning traces collected from student interactions with courses hosted on a Moodle platform. The results highlight a significant improvement in predictive performance (a coefficient of determination of 87%, an average precision of 89.2%, an overall recall of 87.5%, an F1-score of 88.3%, and an accuracy of 90.4%) compared to traditional approaches. This work underscores the value of hybrid techniques for advanced educational data analysis. The results open promising perspectives for integrating intelligent learning support systems, as well as for adapting and extending the model to other educational contexts, learning platforms, and academic levels.
Keywords
Online learning, academic performance prediction, hybrid machine learning, explainable generative AI, SHAP
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