TY  - JOUR
T1  - A HYBRID MODEL BASED ON MACHINE LEARNING AND GENERATIVE AI FOR PREDICTING ACADEMIC PERFORMANCE FROM LEARNING TRACES
AU  - Rafiq, Jamal Eddine
AU  - Zakrani, Abdelali
AU  - Nouh, Said
PY  - 2026
DA  - July
Y2  - 2025
DO  - 10.17718/tojde.1771311
JF  - Turkish Online Journal of Distance Education
JO  - TOJDE
PB  - Anadolu University
WT  - DergiPark
SN  - 1302-6488
SP  - 85
EP  - 95
VL  - 27
IS  - 3
LA  - en
AB  - 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.
KW  - Online learning
KW  - academic performance prediction
KW  - hybrid machine learning
KW  - explainable
generative AI
KW  - SHAP
CR  - Al-tameemi, G. (2024). A Hybrid Machine Learning Approach for Predicting Student A Hybrid Machine Learning Approach for Predicting Student Performance Using Multi-class Educational Datasets. Procedia Computer Science, 238(2019), 888–895. https://doi.org/10.1016/j.procs.2024.06.108
CR  - Al-Zawqari, A., Peumans, D., & Vandersteen, G. (2022). A flexible feature selection approach for predicting students’ academic performance in online courses. Computers and Education: Artificial Intelligence, 3(May), 100103. https://doi.org/10.1016/j.caeai.2022.100103
CR  - Albreiki, B., Zaki, N., & Alashwal, H. (2021). A systematic literature review of student’ performance prediction using machine learning techniques. Education Sciences, 11(9). https://doi.org/10.3390/educsci11090552
CR  - Ali, R. H. (2022). Educational Data Mining For Predicting Academic Student Performance Using Active Classification. Iraqi Journal of Science, 63(9), 3954–3965. https://doi.org/10.24996/ijs.2022.63.9.27
CR  - Azizah, Z., Ohyama, T., Zhao, X., Ohkawa, Y., & Mitsuishi, T. (2024). Predicting at-risk students in the early stage of a blended learning course via machine learning using limited data. Computers and Education: Artificial Intelligence, 100261. https://doi.org/10.1016/j.caeai.2024.100261
UR  - https://doi.org/10.17718/tojde.1771311
L1  - https://dergipark.org.tr/en/download/article-file/5182554
ER  -