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Europe and MENA Cooperation Advances in Information and Communication Technologies pp 87–94Cite as

Educational Data Mining: A Literature Review

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 520)

Abstract

The adoption of learning management systems in education has been increasing in the last few years. Various data mining techniques like prediction, clustering and relationship mining can be applied on educational data to study the behavior and performance of the students. This paper explores the different data mining approaches and techniques which can be applied on Educational data to build up a new environment give new predictions on the data. This study also looks into the recent applications of Big Data technologies in education and presents a literature review on Educational Data Mining and Learning Analytics.

Keywords

  • EDM
  • Prediction
  • Clustering
  • Relationship mining
  • Learning management systems

Track2—ARTIFICIAL INTELLIGENCE IN EDUCATION Distributed Artificial Intelligence in education (DAIED) and Web-based AIED systems.

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Authors and Affiliations

  1. Centre of Technologies and Systems (CTS) of Uninova, Lisbon, Portugal

    Carla Silva & José Fonseca

Authors
  1. Carla Silva
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  2. José Fonseca
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Corresponding author

Correspondence to Carla Silva .

Editor information

Editors and Affiliations

  1. Fac de Ciências e Tech Dept de Enge Info, Universidade de Coimbra, Coimbra, Portugal

    Álvaro Rocha

  2. Présidence de l'Université Mohammed Prem, University Mohammed First Oujda, Oujda, Morocco

    Mohammed Serrhini

  3. (CIETI) & (ISEP), School of Engg Polytechnic of Porto, Porto, Portugal

    Carlos Felgueiras

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Silva, C., Fonseca, J. (2017). Educational Data Mining: A Literature Review. In: Rocha, Á., Serrhini, M., Felgueiras, C. (eds) Europe and MENA Cooperation Advances in Information and Communication Technologies. Advances in Intelligent Systems and Computing, vol 520. Springer, Cham. https://doi.org/10.1007/978-3-319-46568-5_9

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  • DOI: https://doi.org/10.1007/978-3-319-46568-5_9

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