Educational Data Mining (EDM)

Abstract: Educational Data Mining (EDM) is an emerging interdisciplinary research area and with the essence of data mining concepts, it deals with the processing and knowledge acquisition from the data originating in educational context. EDM uses computational approaches to process and analyze educational data to study educational questions. Distributed Data Mining (DDM) plays important role because of; first, mining requires huge amounts of resources in storage space and computation time. To make systems scalable, it is important to develop mechanisms which distribute the work load among several sites. Second, data is often inherently distributed among several sites, making centralized processing of this data may prone to security risks

For better decision making, the data collected from large repositories of different applications require proper method of extracting knowledge. There are increasing research interests in using data mining in education. This new emerging field, called Educational Data Mining, concerns with developing methods that discover knowledge from data originating from educational environments [3]. The Educational Data Mining (EDM) aims at the discovery of useful information from large collections of data [1]. Educational Data Mining uses many techniques such as Decision Trees, Neural Networks, Naïve Bayes, K- Nearest neighbor, and many others for classification, prediction, association rule generation, discover and extract patterns of stored data [2]. The discovered knowledge can be used for prediction regarding enrolment of students in a particular course, faculty performance analysis, placement prediction, prediction about students’ performance and so

There have been various works in this area involving different techniques of data mining.

A. Evaluation of Students academic performance Shreenath Acharya et al. [4] presented an overview on Knowledge Discovery on Databases to predict the student’s academic trends. Lots of work has been done in this using data mining. Using the historical information and the current semester data students’ performance can be predicted. Many of association rule mining algorithms can be used to generate the frequent item sets which can be useful to predict the students’ performance. Kalpesh Adhatrao et al.[5] have applied different decision tree algorithms like ID3(Iterative Dichotomiser) and C4.5 to predict students academic performances and checked their accuracy to choose the appropriate algorithm that could be effectively applied. Mohammed M. Abu Tair and Alaa M. El-Halees [6] have given a case study on how to use different data mining techniques like Association Rule Mining, Clustering, Classification and Outlier Detection at various phases to improve the academic performance of graduate