research-article

Social learning analytics: five approaches

Authors:

Rebecca Ferguson and

Simon Buckingham ShumAuthors Info & Claims

LAK '12: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge

April 2012

Pages 23 - 33

https://doi.org/10.1145/2330601.2330616

Published: 29 April 2012 Publication History

Abstract

This paper proposes that Social Learning Analytics (SLA) can be usefully thought of as a subset of learning analytics approaches. SLA focuses on how learners build knowledge together in their cultural and social settings. In the context of online social learning, it takes into account both formal and informal educational environments, including networks and communities. The paper introduces the broad rationale for SLA by reviewing some of the key drivers that make social learning so important today. Five forms of SLA are identified, including those which are inherently social, and others which have social dimensions. The paper goes on to describe early work towards implementing these analytics on SocialLearn, an online learning space in use at the UK's Open University, and the challenges that this is raising. This work takes an iterative approach to analytics, encouraging learners to respond to and help to shape not only the analytics but also their associated recommendations.

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Index Terms

Social learning analytics: five approaches
1. Applied computing
  1. Education

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Published In

cover image ACM Conferences

LAK '12: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge

April 2012

282 pages

ISBN:9781450311113

DOI:10.1145/2330601

Conference Chairs:
Shane Dawson
University of British Columbia, Canada
,
Caroline Haythornthwaite
University of British Columbia, Canada
,
Editors:
Simon Buckingham Shum,
Dragan Gasevic,
Rebecca Ferguson

Copyright © 2012 ACM.

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 [email protected]

Sponsors

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
TEKRI: Technology-Enhanced Knowledge Research Institute
Desire2Learn: Desire2Learn Inc.
EDUCAUSE: EDUCAUSE
University of British Columbia: University of British Columbia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 April 2012

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LAK 2012

Sponsor:

SIGWEB
TEKRI
Desire2Learn
EDUCAUSE
University of British Columbia

LAK 2012: Second International Conference on Learning Analytics and Knowledge

April 29 - May 2, 2012

British Columbia, Vancouver, Canada

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https://doi.org/10.5817/SP2023-3-6
Goenka NPatnaik DPradhan B(2024)Impact of Artificial Intelligence on Virtual Teacher Learner EngagementAI Algorithms and ChatGPT for Student Engagement in Online Learning10.4018/979-8-3693-4268-8.ch013(192-207)Online publication date: 14-Jun-2024
https://doi.org/10.4018/979-8-3693-4268-8.ch013
Juhaňák L(2023)Analytika učení a data mining ve vzdělávání v kontextu systémů pro řízení výuky10.5817/CZ.MUNI.M280-0185-2023Online publication date: 2023
https://doi.org/10.5817/CZ.MUNI.M280-0185-2023
Yadav M(2023)Mining student coding behaviors in a programming MOOC: there are no actionable learner stereotypesEducational Technology Quarterly10.55056/etq.611Online publication date: 26-Oct-2023
https://doi.org/10.55056/etq.611
Al-Zahrani A(2023)Empowering Girls' Higher Education Through Social Learning PlatformsInternational Journal of Virtual and Personal Learning Environments10.4018/IJVPLE.33138313:1(1-16)Online publication date: 2-Oct-2023
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