Output from Statistical Predictive Models as Input to eLearning Dashboards

Marlene A. Smith
Additional contact information
Marlene A. Smith: Business School, University of Colorado Denver, 1475 Lawrence Street, Denver, CO 80202, USA

Future Internet, 2015, vol. 7, issue 2, 1-14

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.

Keywords: eLearning; analytics; dashboards; big data; predictive models; statistical models; data mining; massive open online courses; microtargeting (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1999-5903/7/2/170/pdf (application/pdf)
https://www.mdpi.com/1999-5903/7/2/170/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:7:y:2015:i:2:p:170-183:d:50583

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().