Educational Data Mining techniques and their applications

Abstract

Educational Data Mining (EDM) is a learning science, and an emerging discipline, concerned with analyzing and studying data from academic databases. Through the exploration of these large datasets, using various data mining methods, one can identify unique patterns which will help study, predict and improve a student's academic performance. This paper elaborates a study on various Educational Data Mining techniques and how they could be used for the benefit of all the stakeholders in the educational system. Correlation is used to see if a variation in one variable results in a variation in the other. Decision trees give possible outcomes and are used to predict students' performance in this study. Regression analysis is used in the construction of a model involving a dependent variable and multiple independent variables; if the model is satisfactory, then the value of dependent variable is determined using the values of the independent variables. Clustering finds groups of objects so that objects that are in a cluster are more like each other than to objects in another cluster, helping in arranging items under consideration; clustering would help in analyzing the job profiles that would be suited for each student.