• Abelardo PardoUniversity of South Australiahttp://orcid.org/0000-0002-6857-0582
• Kathryn BartimoteThe University of Sydney
• Simon Buckingham ShumUniversity of Technology Sydney
• Shane DawsonUniversity of South Australia
• Jing GaoUniversity of South Australia
• Dragan GaševićMonash University
• Steve LeichtweisUniversity of Auckland
• Danny LiuThe University of Sydney
• Roberto Martínez-MaldonadoUniversity of Technology Sydney
• Negin MirriahiUniversity of South Australia
• Adon Christian Michael MoskalOtago Polytechnic
• Jurgen SchulteUniversity of Technology Sydney
• George SiemensUniversity of South Australia
• Lorenzo VigentiniUniversity of New South Wales

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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.

Author Biography