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Department of Artificial Intelligence and Data Engineering Luxembourg Institute of Digital Intelligence Luxembourg City, Luxembourg
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Center for Machine Learning Research European AI Innovation Laboratory Esch-sur-Alzette, Luxembourg
Abstract
The increasing complexity of higher education environments has created a strong demand for data-driven approaches capable of identifying the factors influencing student achievement. Academic performance is shaped by multiple interacting elements, including learning behaviors, educational resources, technological engagement, and individual academic characteristics. This research presents an intelligent classification approach for identifying key determinants of student achievement in higher education by integrating educational data mining concepts and artificial intelligence-based classification strategies. The study synthesizes existing research on educational profiling, performance prediction, technology-supported learning, and dropout analysis to establish a conceptual framework for analyzing academic success patterns. Previous studies demonstrate that classification techniques can reveal hidden relationships between learner characteristics and educational outcomes, supporting evidence-based academic decision-making (Akazaki et al., 2020; Yılmaz & Sekeroglu, 2020). The proposed approach emphasizes feature identification, learner categorization, and predictive interpretation to support early intervention and personalized educational strategies. Findings indicate that intelligent classification models can enhance institutional understanding of student achievement determinants while addressing challenges related to data complexity, interpretability, and contextual variation. The research contributes a structured analytical perspective for higher education institutions seeking to improve academic outcomes through intelligent decision-support mechanisms.
How to Cite
Dr. Julien Weber, & Dr. Sophie Muller. (2026). Intelligent Classification Approach for Identifying Key Determinants of Student Achievement in Higher Education. Frontiers in Emerging Multidisciplinary Sciences, 3(08), 25–34. Retrieved from https://irjernet.com/index.php/fems/article/view/483
Akazaki, J. M., Moro, F. F., Tarouco, L. M. R., Behar, P. A., Machado, L. R., & Cazella, S. C. (2020). Educational data mining for the profile of candidates from the Encceja in the state of Rio Grande do Sul–Brazil. Redin - Práticas Educacionais e Inovação em Tempo de Isolamento Social, 9(1), 175–187.
de Oliveira, K. L., Boruchovitch, E., & dos Santos, A. A. A. (2008). Leitura e desempenho escolar em português e matemática no ensino fundamental [Reading and school performance in Portuguese and mathematics in elementary school]. Paidéia, 18(41), 531–540.
Lima, M. N., & Fagundes, R. A. D. A. (2020). Educational data mining: A study of the factors that cause school dropout in higher education institutions in Brazil. Revista Novas Tecnologias Na Educação, 18(1).
Matos, J. D. V., Cruz, J. R., Ribeiro, A. F. S., Gomes, R. M. M., Ferreira, J. C., & Matos, F. B. (2019). Aprendizagem significativa por meio do uso de TICs: Levantamento das produções da area de ensino de 2016 a 2018 [Meaningful learning through the use of ICTs: Survey of productions in the teaching area from 2016 to 2018]. Revista Novas Tecnologias Na Educação, 17(1), 466–475.
Sendacz, N., Isotani, S., & Lima, D. A. (2022). Literature review on technologies and games that motivated people to practice physical activity during the pandemic. Revista Novas Tecnologias na Educação, 20(2), 280–289.
Yılmaz, N., & Sekeroglu, B. (2020). Student performance classification using artificial intelligence techniques. In 10th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions, 596–603.
Akazaki, J. M., Moro, F. F., Tarouco, L. M. R., Behar, P. A., Machado, L. R., & Cazella, S. C. (2020). Educational data mining for the profile of candidates from the Encceja in the state of Rio Grande do Sul–Brazil. Redin - Práticas Educacionais e Inovação em Tempo de Isolamento Social, 9(1), 175–187.
de Oliveira, K. L., Boruchovitch, E., & dos Santos, A. A. A. (2008). Leitura e desempenho escolar em português e matemática no ensino fundamental [Reading and school performance in Portuguese and mathematics in elementary school]. Paidéia, 18(41), 531–540.
Lima, M. N., & Fagundes, R. A. D. A. (2020). Educational data mining: A study of the factors that cause school dropout in higher education institutions in Brazil. Revista Novas Tecnologias Na Educação, 18(1).
Matos, J. D. V., Cruz, J. R., Ribeiro, A. F. S., Gomes, R. M. M., Ferreira, J. C., & Matos, F. B. (2019). Aprendizagem significativa por meio do uso de TICs: Levantamento das produções da area de ensino de 2016 a 2018 [Meaningful learning through the use of ICTs: Survey of productions in the teaching area from 2016 to 2018]. Revista Novas Tecnologias Na Educação, 17(1), 466–475.
Sendacz, N., Isotani, S., & Lima, D. A. (2022). Literature review on technologies and games that motivated people to practice physical activity during the pandemic. Revista Novas Tecnologias na Educação, 20(2), 280–289.
Yılmaz, N., & Sekeroglu, B. (2020). Student performance classification using artificial intelligence techniques. In 10th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions, 596–603.
K. Ramamurthy, R. K. Konduru and N. Amanmadov, "Evo Graph Coder: An Evolutionary Graph-Reasoning Framework for Self-Adaptive Software Engineering," in IEEE Access, vol. 14, pp. 63063-63076, 2026, doi: 10.1109/ACCESS.2026.3686019.
M. R. Marri, S. B. Kurada, S. Gupta, S. Dey, S. Kumar and R. Chauhan, "Graph-Based Deep Learning Model for Identifying Cyber Threats in Cloud Platforms," 2025 International Conference on Computational Intelligence, Security, and Artificial Intelligence (IntelliSecAI), Al-Khobar, Saudi Arabia, 2025, pp. 1-7, doi: 10.1109/IntelliSecAI66368.2025.11472831.
