aspg-18-2159-1792095983 20261015202623 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2023 10 2 Rahul Sharma Department of Computer Science and Engineering, Rabindranath Tagore University, Raisen, (M.P.), India Shiv Shakti Shrivastava Department of Computer Science and Engineering, Rabindranath Tagore University, Raisen, (M.P.), India Aditi Sharma Department of Computer Science and Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India; IEEE Senior Member, Symbiosis Institute of Techno Data analysis is an essential component of decision support in various industries that includes industrial and educational institutions. This research proposes Data Mining (DM) techniques to improve the efficiency of higher education (HE) institutions. DM has a substantial impact on different higher education activities including student performances, management of student’s life cycle, selection of courses, monitoring of retention rate, grants & funds management by using technique’s such as clustering, decision trees (DT), and association. Educational Data Mining (EDM) is an interdisciplinary study topic that focuses on getting DM to the fields of education by leveraging methods from (ML) statistics, (DM), and (DA) to get important insights from educational sets of data. EDM is critical in transforming raw data into useful information, allowing for a greater knowledge of students and their academic settings, as well as promoting better teacher assistance and ESD (Educational System Decisions). The study's goal is to provide a complete overview of EDM (Educational Data Mining), highlighting its various applications and benefits in the context of higher education. 2023 24 37 2159 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.100203 https://www.americaspg.com/journal/18/article/2159 https://www.americaspg.com/storage/61697535115.pdf