OnTask: delivering data-informed, personalized learning support actions

Abelardo Pardo, Kathryn Bartimote, Simon Buckingham Shum, Shane Dawson, Jing Gao, Dragan Gašević, Steve Leichtweis, Danny Liu, Roberto Martinez-Maldonado, Negin Mirriahi, Adon Christian Michael Moskal, Jurgen Schulte, George Siemens, Lorenzo Vigentini

Research output: Contribution to journalArticleResearchpeer-review

Abstract

The learning analytics community has matured significantly over the past few years as a middle space where technology and pedagogy combine to support learning experiences. To continue to grow and connect these perspectives, research needs to move beyond the level of basic support actions. This means exploring the use of data to prove richer forms of actions, such as personalized feedback, or hybrid approaches where instructors interpret the outputs of algorithms and select an appropriate course of action. This paper proposes the following three contributions to connect data extracted from the learning experience with such personalized student support actions: 1) a student–instructor centred conceptual model connecting a representation of the student information with a basic set of rules created by instructors to deploy Personalized Learning Support Actions (PLSAs); 2) a software architecture based on this model with six categories of functional blocks to deploy the PLSAs; and 3) a description of the implementation of this architecture as an open-source platform to promote the adoption and exploration of this area.
Original languageEnglish
Pages (from-to)235-249
Number of pages15
JournalJournal of Learning Analytics
Volume5
Issue number3
DOIs
Publication statusPublished - 11 Dec 2018

Keywords

  • Learning analytics
  • feedback
  • personalization
  • open source
  • student support

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