Future Internet 2015, 7(2), 170-183; doi:10.3390/fi7020170
Output from Statistical Predictive Models as Input to eLearning Dashboards
Business School, University of Colorado Denver, 1475 Lawrence Street, Denver, CO 80202, USA
Academic Editor: Liz Bacon
Received: 1 March 2015 / Accepted: 11 May 2015 / Published: 2 June 2015
(This article belongs to the Special Issue eLearning)
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. View Full-TextKeywords:
eLearning; analytics; dashboards; big data; predictive models; statistical models; data mining; massive open online courses; microtargeting
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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MDPI and ACS Style
Smith, M.A. Output from Statistical Predictive Models as Input to eLearning Dashboards. Future Internet 2015, 7, 170-183.
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