OnTask: Delivering Data-Informed, Personalized Learning Support Actions

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. Notes for practice include the following: (1) The report draws on research findings related to the effect of personalized feedback on student satisfaction and academic performance (Pardo, Jovanovic, Dawson, Gaševic, & Mirriahi, 2018); (2) The main contribution is the description of the design and implementation of an open source platform for researchers and practitioners to connect data with personalized learning support actions; (3) The area of learning analytics needs tools such as the one described in this document to serve as a vehicle to exchange insights among researchers and practitioners; and (4) This is an example of the note for practice and research.