OnTask: Delivering Data-Informed, Personalized Learning Support Actions

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

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.


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DOI: https://doi.org/10.18608/jla.2018.53.15

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