A Survey on Tools and Techniques of Classification in Educational Data Mining

Educational Data Mining (EDM) is one of the newest topics to emerge in recent years, and it focuses on developing strategies for analyzing various forms of data gathered from the academic circle. EDM fosters collaboration among educators, data scientists, and machine learning specialists. The interdisciplinary character of EDM creates an atmosphere in which educators and data scientists collaborate to develop and apply efficient approaches for extracting insights from educational data. EDM methods and techniques with Machine learning techniques are utilized to extract meaningful and useful information from large dataset. EDM strives to develop intelligent systems that personalize educational experiences to individual individuals by understanding their unique learning styles and problems. For scientists and researchers, realistic applications of Machine Learning in the EDM sectors offer new frontiers and present new problems. This transition to individualized approaches represents a radical shift in educational practices, stressing a student-centered and successful learning environment. One of the most important research areas in EDM is predicting student success. The prediction algorithms and techniques must be developed, to forecast students’ performance, which aids the tutor, institution to boost the level of students’ performance. Beyond forecasting student achievement, EDM is increasingly focusing on the creation of personalized learning systems and adaptive educational technology. EDM’s goal is to construct intelligent systems that personalize educational experiences to individual students by utilizing classification algorithms and data mining tools. This paper examines various classification techniques in prediction methods and data mining tools used in EDM.