A Review on EDM Techniques with Special Focus on Student Performance Enhancement - John, Simmi; Mohamed, Anuj (International Journal of Computer Sciences and Engineering, 2019)
Today many of the institutions use data mining techniques, especially in the field of education. The main purpose of Educational Data Mining (EDM) is to increase the quality of education. Use of data mining methods in educational scenarios will help us to learn student behaviour, their performance, enhance the present student models and efficiently design the course curriculum. Teachers will get an overview into the academic performance and administrators can make policies, execute programmes, and adapt the policies and programmes to enhance the teaching–learning process. Using EDM we can improve student’s achievements and success more efficiently and effectively. Machine Learning methods are very efficient for predicting student performance. The student data depends on the various educational environments. Selection of the correct dataset plays a vital role in these predictions. EDM uses computational approaches to analyze educational problems and data. By applying data mining techniques we can extract valuable information from huge amounts of data. For extracting knowledge from huge volume of data we require sophisticated set of algorithms and data pre-processing techniques. This paper surveys the most relevant studies carried out in the field of student performance enhancement. It also discusses EDM, areas of student performance enhancements and enhancement methods based on classification.