Learning analytics beyond the LMS : the Connected Learning Analytics toolkit

Kitto, Kirsty, Cross, Sebastian, Waters, Zak, & Lupton, Mandy (2015) Learning analytics beyond the LMS : the Connected Learning Analytics toolkit. In Proceedings of the 5th International Learning Analytics and Knowledge (LAK) Conference, ACM, Poughkeepsie, New York, USA.

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We present a Connected Learning Analytics (CLA) toolkit, which enables data to be extracted from social media and imported into a Learning Record Store (LRS), as defined by the new xAPI standard. Core to the toolkit is the notion of learner access to their own data. A number of implementational issues are discussed, and an ontology of xAPI verb/object/activity statements as they might be unified across 7 different social media and online environments is introduced. After considering some of the analytics that learners might be interested in discovering about their own processes (the delivery of which is prioritised for the toolkit) we propose a set of learning activities that could be easily implemented, and their data tracked by anyone using the toolkit and a LRS.

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ID Code: 81343
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: Connected Learning, xAPI, integration, data ownership, HERN
DOI: 10.1145/2723576.2723627
ISBN: 9781450334174
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600) > Computer-Human Interaction (080602)
Australian and New Zealand Standard Research Classification > EDUCATION (130000) > SPECIALIST STUDIES IN EDUCATION (130300) > Educational Technology and Computing (130306)
Australian and New Zealand Standard Research Classification > PSYCHOLOGY AND COGNITIVE SCIENCES (170000) > COGNITIVE SCIENCE (170200) > Knowledge Representation and Machine Learning (170203)
Divisions: Current > QUT Faculties and Divisions > Faculty of Education
Current > Institutes > Institute for Future Environments
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2015 Kirsty Kitto
Copyright Statement: Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org.
Deposited On: 02 Feb 2015 23:14
Last Modified: 22 Jun 2017 02:49

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