Data mining in education: a comprehensive review

Abstract

Schools and colleges collect vast amounts of data from learning management systems, online learning platforms, assessment systems, and administration databases. Educational Data Mining (EDM) is a newly developing research area that is associated with the application of data mining techniques to educational data, the extraction of useful information from it, and the use of the information to enhance both instruction and learning as well as the decision-making processes of an educational institution. This review paper aims to present a comprehensive survey of the area of data mining applications, techniques, problems, and future directions in the educational sector. The current research focused on secondary data analysis of past studies grounded in the EDM and learning analytics (LA) field. Sources such as various published papers, journal articles, and conference papers were reviewed to find commonly used techniques, applications, and new trends in the field. We review some techniques like classification, clustering, association rule mining, predictive modeling, and LA. The results show that the role of this EDM in predicting students’ learning abilities, dropout rate detection, recommendation systems for student learning, and tools in curriculum designing has an important role. Moreover, data mining is used in the field of intelligent tutoring systems and in the analysis of the performance of institutions. The paper also identifies some significant issues in the field of EDM, such as data quality, privacy and ethical issues, and the requirement for understandable and accurate algorithms. In addition, new areas of research and development in educational technology are identified, including real-time analytics, multimodal data analysis, artificial intelligence (AI), and explainable AI. To summarize, by converting raw educational data into insightful information, EDM can be helpful to enhance the teaching, learning, and decision-making processes in educational activities and policies. The study underscores the need to use ethical and safe education data that could continue in the future for educational development while, at the same time, using effective data mining techniques.