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Output from Statistical Predictive Models as Input to eLearning Dashboards

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  • Marlene A. Smith

    (Business School, University of Colorado Denver, 1475 Lawrence Street, Denver, CO 80202, USA)

Abstract

We describe how statistical predictive models might play an expanded role in educational analytics by giving students automated, real-time information about what their current performance means for eventual success in eLearning environments. We discuss how an online messaging system might tailor information to individual students using predictive analytics. The proposed system would be data-driven and quantitative; e.g., a message might furnish the probability that a student will successfully complete the certificate requirements of a massive open online course. Repeated messages would prod underperforming students and alert instructors to those in need of intervention. Administrators responsible for accreditation or outcomes assessment would have ready documentation of learning outcomes and actions taken to address unsatisfactory student performance. The article’s brief introduction to statistical predictive models sets the stage for a description of the messaging system. Resources and methods needed to develop and implement the system are discussed.

Suggested Citation

  • Marlene A. Smith, 2015. "Output from Statistical Predictive Models as Input to eLearning Dashboards," Future Internet, MDPI, vol. 7(2), pages 1-14, June.
    • Handle: RePEc:gam:jftint:v:7:y:2015:i:2:p:170-183:d:50583
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    References listed on IDEAS

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    1. David J. Hand & Heikki Mannila & Padhraic Smyth, 2001. "Principles of Data Mining," MIT Press Books, The MIT Press, edition 1, volume 1, number 026208290x, April.
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