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Framing Learning Analytics and Educational Data Mining for Teaching: Critical Inferencing, Domain Knowledge, and Pedagogy

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Frontiers of Cyberlearning

Part of the book series: Lecture Notes in Educational Technology ((LNET))

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Abstract

This chapter reviews key challenges of learning analytics and educational data mining. It highlights early generation learning analytics pitfalls that could compromise the future of their use in technology-delivered instruction, especially if teachers are not well trained and adequately equipped with both technical and sociocritical literacy of this new field. Among the issues are potential for bias and inaccuracy in the algorithms involved, the propensity toward closed proprietary systems whose algorithms cannot be scrutinized, and the paucity of learning models typically considered. The new learning analytics and educational data mining systems bring with them a set of claims, aspirations, and mystique. These underlying technologies could be harbingers of future breakthroughs: a new generation of artificial intelligence systems adaptively responding to students’ interactions with online teaching environments.

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Author information

Authors and Affiliations

  1. Educational Technology Services, UC Berkeley, Berkeley, CA, USA

    Owen G. McGrath

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  1. Owen G. McGrath
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Correspondence to Owen G. McGrath .

Editor information

Editors and Affiliations

  1. Department of Learning Technologies, University of North Texas, Denton, TX, USA

    J. Michael Spector

  2. School of Computing and Information System, Athabasca University, Athabasca, Canada

    Vivekanandan Kumar

  3. Analytics and R&D, McGraw-Hill Education, Boston, MA, USA

    Alfred Essa

  4. Department of Engineering Science, National Cheng Kung University, Tainan, Taiwan

    Yueh-Min Huang

  5. Open University in the Netherlands, Heerlen, The Netherlands

    Rob Koper

  6. School of Computing, University of Eastern Finland, Joensuu, Finland

    Richard A. W. Tortorella

  7. Smart Learning Institute, Beijing Normal University, Beijing, China

    Ting-Wen Chang

  8. School of Educational Technology, Beijing Normal University, Beijing, China

    Yanyan Li

  9. School of Educational Technology, Beijing Normal University, Beijing, China

    Zhizhen Zhang

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McGrath, O.G. (2018). Framing Learning Analytics and Educational Data Mining for Teaching: Critical Inferencing, Domain Knowledge, and Pedagogy. In: Spector, J., et al. Frontiers of Cyberlearning. Lecture Notes in Educational Technology. Springer, Singapore. https://doi.org/10.1007/978-981-13-0650-1_2

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  • DOI: https://doi.org/10.1007/978-981-13-0650-1_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-0649-5

  • Online ISBN: 978-981-13-0650-1

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