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Educational Data Mining and Learning Analytics

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Abstract

In recent years, two communities have grown around a joint interest on how big data can be exploited to benefit education and the science of learning: Educational Data Mining and Learning Analytics. This article discusses the relationship between these two communities, and the key methods and approaches of educational data mining. The article discusses how these methods emerged in the early days of research in this area, which methods have seen particular interest in the EDM and learning analytics communities, and how this has changed as the field matures and has moved to making significant contributions to both educational research and practice.

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Notes

  1. 1.

    A fourth type of structure discovery, Network Analysis, is more characteristic of work in learning analytics than in educational data mining (cf. Dawson 2008; Suthers and Rosen 2011), and is not discussed in detail here for that reason.

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Authors and Affiliations

  1. Teachers College, Columbia University, New York, NY, USA

    Ryan Shaun Baker

  2. Center for Empathic Human-Computer Interactions, College of Computer Studies, De La Salle University, Manila, Philippines

    Paul Salvador Inventado

  3. Architecture for Intelligence, The Institute of Scientific and Industrial Research, Osaka University, Osaka, Japan

    Paul Salvador Inventado

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  1. Ryan Shaun Baker
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Correspondence to Ryan Shaun Baker .

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Editors and Affiliations

  1. Center for Digital Data, Analytics and Adaptive Learning at Pearson, Boston, Massachusetts, USA

    Johann Ari Larusson

  2. Dept. of English, University of California Berkeley, Berkeley, California, USA

    Brandon White

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Baker, R.S., Inventado, P.S. (2014). Educational Data Mining and Learning Analytics. In: Larusson, J., White, B. (eds) Learning Analytics. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3305-7_4

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