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

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Educational Data Mining

Part of the book series: Studies in Computational Intelligence ((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.

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Notes

  1. 1.

    http://www.educationaldatamining.org/JEDM/ visited on August 6, 2013.

  2. 2.

    https://pslcdatashop.web.cmu.edu/ visited on August 6, 2013.

  3. 3.

    http://mulce-pf.univ-fcomte.fr/PlateFormeMulce/visited on August 6, 2013.

  4. 4.

    http://www.cs.waikato.ac.nz/ml/weka/ visited on August 6, 2013.

  5. 5.

    http://www.r-project.org visited on August 6, 2013.

  6. 6.

    http://www.pentaho.com visited on August 6, 2013.

  7. 7.

    http://rapid-i.com visited on August 6, 2013.

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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  1. Laboratoire Méthode de Conception de Systèmes (LMCS), Ecole nationale Supérieure d’Informatique (ESI), BP 68M, 16309, Oued-Smar, Algiers, Algeria

    Nabila Bousbia & Idriss Belamri

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  1. Nabila Bousbia
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    Alejandro Peña-Ayala

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Bousbia, N., Belamri, I. (2014). Which Contribution Does EDM Provide to Computer-Based Learning Environments?. In: Peña-Ayala, A. (eds) Educational Data Mining. Studies in Computational Intelligence, vol 524. Springer, Cham. https://doi.org/10.1007/978-3-319-02738-8_1

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