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Introduction to the special section on educational data mining

Authors:

Toon Calders,

Mykola PechenizkiyAuthors Info & Claims

ACM SIGKDD Explorations Newsletter, Volume 13, Issue 2

Pages 3 - 6

https://doi.org/10.1145/2207243.2207245

Published: 01 May 2012 Publication History

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Abstract

Educational Data Mining (EDM) is an emerging multidisciplinary research area, in which methods and techniques for exploring data originating from various educational information systems have been developed. EDM is both a learning science, as well as a rich application area for data mining, due to the growing availability of educational data. EDM contributes to the study of how students learn, and the settings in which they learn. It enables data-driven decision making for improving the current educational practice and learning material. We present a brief overview of EDM and introduce four selected EDM papers representing a crosscut of different application areas for data mining in education.

References

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G. Dekker, M. Pechenizkiy, and J. Vleeshouwers. Predicting students drop out: A case study. In Proceedings of the 2nd International Conference on Educational Data Mining, EDM'09, pages 41--50, 2009.

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M. Pechenizkiy, T. Calders, C. Conati, S. Ventura, C. Romero, and J. Stamper, editors. Proceedings of the 4th International Conference on Educational Data Mining, Eindhoven, the Netherlands, July 6-8, 2011, 2011.

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Published In

ACM SIGKDD Explorations Newsletter Volume 13, Issue 2

December 2011

101 pages

ISSN:1931-0145

EISSN:1931-0153

DOI:10.1145/2207243

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Association for Computing Machinery

New York, NY, United States

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Published: 01 May 2012

Published in SIGKDD Volume 13, Issue 2

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Liu LWang SBritton TAbebe R(2023)Reimagining the machine learning life cycle to improve educational outcomes of studentsProceedings of the National Academy of Sciences10.1073/pnas.2204781120120:9Online publication date: 24-Feb-2023
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