Which Contribution Does EDM Provide to Computer-Based Learning Environments?

Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 524)

Abstract

Educational Data Mining is a new growing research area that can be defined as the application of data mining techniques on raw data from educational systems in order to respond to the educational questions and problems, and also to discover the information hidden after this data. Over the last few years, the popularity of this field enhanced a large number of research studies that is difficult to surround and to identify the contribution of data mining techniques in educational systems. In fact, exploit and understand the raw data collected from educational systems can be “a gold mine” to help the designers and the users of these systems improving their performance and extracting useful information on the behaviors of students in the learning process. The use of data mining techniques in e-learning systems could be very interesting to resolve learning problems. Researchers’ ambition is to respond to questions like: What can predict learners’ success? Which scenario sequence is more efficient for a specific student? What are the student actions that indicate the learning progress? What are the characteristics of a learning environment allowing a better learning? etc. The current feedback allows detecting the usefulness of applying EDM on visualizing and describing the learning raw data. The predictions take also an interest, particularly the prediction of performance and learners’ behaviors. The aim of this chapter is to establish a bibliographic review of the various studies made in the field of educational data mining (EDM) to identify the different aspects studied: the analyzed data, the objectives of these studies, the used techniques and the contribution of the application of these techniques in the field of computer based learning. The goal is not only to list the existing work but also to facilitate the use and the understanding of data mining techniques to help the educational field specialists to give their feedback and to identify promoter research areas in this field to be exploited in the future.

Keywords

EDM Learner’s behavior Prediction of student’s performance Computer based learning environments (CBLE) 

Abbreviations

CBLE

Computer based learning environment

DM

Data mining

EDM

Educational data mining

ITS

Intelligent tutoring system

KDD

Knowledge discovery in databases

KT

Knowledge tracing

LA

Learning analytics

LAK

Learning analytics and knowledge

LMS

Learning management system

NMF

Non-negative matrix factorization

SNA

Social network analysis

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Laboratoire Méthode de Conception de Systèmes (LMCS)Ecole nationale Supérieure d’Informatique (ESI)Oued-SmarAlgeria

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