Data Mining Methods and Techniques in Higher Education

Abstract: Data analysis is crucial for decision support in every firm, whether it is a manufacturing unit or an educational institution. Data mining techniques are used in a wide range of fields. This paper proposes the use of data mining techniques to improve the efficiency of higher education institutions. When data mining techniques such as clustering, decision trees, and association are used to higher education processes, they can aid in improving student performance, life cycle management, course selection, retention rate monitoring, and grant fund management. This is a strategy for determining how data mining tools affect higher education. Educational Data Mining (EDM) is an interdisciplinary research area focused on data mining's application in the field of education. It uses a number of tools and techniques from machine learning, statistics, data mining, and data analysis to analyses data created during teaching and learning. Educational Data Mining is the process of converting raw data from large educational databases into useful and meaningful information that can be used to better understand students and their learning environments, improve teacher assistance, and make educational system decisions. The goal of this research is to give a broad overview of educational data mining, including its uses and benefits.

Keywords: Educational data mining (EDM), learning analytics (LA), knowledge discovery in databases (KDD), data mining techniques, data mining methods. EDM tools, visualizations tools.