Student Success Prediction Based on Machine Learning and Learning Styles

ac28a00b-1bce-41b6-947c-16eaceaaa40520250718114850192naun:naunmdt@crossref.orgMDT DepositInternational Journal of Education and Information Technologies2074-131610.46300/9109http://www.naun.org/cms.action?id=3037131202513120251910.46300/9109.2025.19https://npublications.com/journals/educationinformation/2025.phpStudent Success Prediction Based on Machine Learning and Learning StylesDijanaOreškiUniversity of Zagreb, Faculty of Organization and Informatics, Pavlinska 2, Varazdin, CroatiaThis study aims to develop a predictive model for student success by integrating machine learning algorithms with learning style analysis. Educational institutions increasingly recognize the value of early performance prediction to implement timely interventions and enhance learning outcomes. Learning management systems generate vast amounts of data. The proposed research will analyze student interaction data from Moodle learning management system, including course logins, resource access patterns, assignment submissions, and assessment performance. These digital footprints will be combined with learning style assessments to identify patterns of academic achievement. Machine learning algorithms are applied and compared to determine the most effective predictive model. This research contributes to educational data mining by exploring the intersection between digital behavior patterns, individual learning preferences, and academic outcomes. The resulting model achieves high prediction accuracy, enabling proactive educational interventions that adapt to students' learning styles while leveraging Moodle's AI capabilities for personalized learning experiences.71820257182025969910https://npublications.com/journals/educationinformation/2025/a222008-011(2025).pdf10.46300/9109.2025.19.10https://npublications.com/journals/educationinformation/2025/a222008-011(2025).pdf10.1109/coginfocom50765.2020.9237894L. Bognár and T. Fauszt, “Different learning predictors and their effects for Moodle machine learning models,” in Proc. 11th IEEE Int. Conf. Cognitive Infocommunications (CogInfoCom), Mariehamn, Finland, Sept. 23–25, 2020, pp. 405–410. doi:10.1109/CogInfoCom50708.2020.9284432 C. Kaensar and W. Wongnin, “Analysis and prediction of student performance based on Moodle log data using machine learning techniques,” Int. J. Emerg. Technol. Learn. (iJET), vol. 18, no. 10, pp. –, 2023. doi:10.3991/ijet.v18i10.35841 10.3390/app9245523L. A. Buschetto Macarini, C. Cechinel, M. F. Batista Machado, V. F. Culmant Ramos, and R. Muñoz, “Predicting students' success in blended learning— Evaluating different interactions inside learning management systems,” Appl. Sci., vol. 9, no. 15, p. 3138, 2019. 10.3389/fpsyg.2021.842357Y. Zhang, Y. Yun, R. An, J. Cui, H. Dai, and X. Shang, “Educational data mining techniques for student performance prediction: Method review and comparison analysis,” Front. Psychol., vol. 12, Art. no. 698490, 2021. L. Baier, F. Jöhren, and S. Seebacher, “Challenges in the deployment and operation of machine learning in practice,” in Proc. 27th Eur. Conf. Inf. Syst. (ECIS), Stockholm, Sweden, Jun. 2019, vol. 1. 10.14569/ijacsa.2024.01501126L. U. Xi, “Modern Education: Advanced Prediction Techniques for Student Achievement Data,” Int. J. Adv. Comput. Sci. Appl., vol. 15, no. 1, pp. –, 2024. doi:10.14569/ijacsa.2024.01501126 S. Gunasekara and M. Saarela, “Explainability in educational data mining and learning analytics: An umbrella review,” in Proc. 17th Int. Conf. Educ. Data Mining (EDM), 2024, pp. 887–892. 10.2991/978-94-6463-136-4_63N. R. Yadav and S. S. Deshmukh, “Prediction of student performance using machine learning techniques: A review,” in Proc. Int. Conf. Appl. Mach. Intell. Data Anal. (ICAMIDA), May 2023, pp. 735–741. Atlantis Press. 10.37394/23202.2023.22.8D. Oreški, “Application of machine learning methods for data analytics in social sciences,” WSEAS Trans. Syst., vol. 22, pp. 69–72, Mar. 7, 2023. doi: 10.37394/23202.2023.22.8 R. Kovač and D. Oreški, “Educational data driven decision making: Early identification of students at risk by means of machine learning,” in Proc. 29th Central European Conf. Inf. Intell. Syst. (CECIIS), Varaždin, Croatia, Sept. 19–21, 2018, pp. 231–237.