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Some of the thinking behind SocialLearn is set out in a chapter in the new book edited by a team at the Open University’s Knowledge Media Institute, with contributions from some of the thought leaders on the future of education:

Collaborative Learning 2.0: Open Educational Resources

Current advances and convergence trends in Web 2.0 have changed the way we communicate and collaborate, and as a result, user-controlled communities and user-generated content through Web 2.0 are expected to play an important role for collaborative learning.

Collaborative Learning 2.0: Open Educational Resources offers a collection of the latest research, trends, future development, and case studies within the field. Without solid theoretical foundation and precise guidelines on how to use OER and Web 2.0 for collaborative learning, it would certainly be very difficult to obtain all the benefits that these “user-generated content, resources and tools” promise. The purpose of this handbook is to understand how OERs and Web 2.0 can be deployed successfully to enrich the collaborative learning experience and ensure a positive outcome in terms of user generated knowledge and development of skills.

In Chapter 17, we set out the rationale that motivates the creation of a platform like SocialLearn…

This chapter examines the meaning of “open” in terms of tools, resources, and education, and goes on to explore the association between open approaches to education and the development of online social learning. It considers why this form of learning is emerging so strongly at this point, what its underlying principles are, and how it can be defined. Openness is identified as one of the motivating rationales for a social media space tuned for learning, called SocialLearn, which is currently being trialed at The Open University in the UK. SocialLearn has been designed to support online social learning by helping users to clarify their intention, ground their learning and engage in learning conversations. The emerging design concept and implementation are described here, with a focus on what personalization means in this context, and on how learning analytics could be used to provide different types of recommendation that support learning.

Read the whole chapter as an open eprint on the OU’s Open Research Online server:

Ferguson, R. and Buckingham Shum, S. (2012). Towards a Social Learning Space for Open Educational Resources. In: Okada, A., Connolly, T. and Scott, P. (Eds.), Collaborative Learning 2.0: Open Educational Resources. Hershey, PA: IGI Global, pp. 309–327. Eprint:

Running free online courses as part of the build-up to f2f conferences seems to be spreading as a powerful strategy to open up the conversation as widely as possible, and generate energy (and light!) prior to meetups. Here are two that I’m involved in, one MOOC and one forum…

With many thanks to Networked Learning 2012, I’m hosting one of the Hotseats in the build-up to the conference — a week-long discussion entitled Learning Analytics: Dream, Nightmare or Fairydust? It kicked off yesterday, and participants are now pitching into the debate after replaying an introductory presentation. It’s free to sign up, so come and join in what’s already a lively conversation!

This meshes nicely with the multi-week MOOC on Learning Analytics, also running as a pre-conference build-up to the 2nd International Conference on Learning Analytics & Knowledge at the end of April. This evening sees the final live webinar (20:00 GMT) looking at the recently formed Society for Learning Analytics Research (SoLAR) to promote a research-oriented approach to LA adoption and use. I led last week’s discussion with an introduction to the SoLAR White Paper arguing for the need to develop an Open Learning Analytics Platform — Webinar replay (launches the Blackboard Collaborate client)

Announcing a new technical report, The State of Learning Analytics in 2012: A Review and Future Challenges.

‘The State of Learning Analytics’ reviews the emergence of the field over the past decade, tracing its roots in other disciplines and setting out current trends and future lines of work. In the process, it identifies the focus of and drivers behind related types of analytic – particularly academic analytics and action analytics.

The development of the field is set out in a broadly chronological structure, which demonstrates the increasingly rapid pattern of development as new drivers emerge, new fields are appropriated and new tools developed. Tracing the development of learning analytics over time highlights a gradual shift away from a technological focus towards an educational focus, and the introduction of tools, initiatives and methods that are significant in the field today.

The report draws on two main sources. The first is the list of resources provided by the online course associated with the LAK 2012 conference. The second is the literature cited in over 70 submissions to that conference. The 20 most-cited works and the 20 most‐cited authors are all included in the report.

Ferguson, R. (2012). The State Of Learning Analytics in 2012: A Review and Future Challenges. Technical Report KMI-12-01, Knowledge Media Institute, The Open University, UK.

The International Conference on Learning Analytics & Knowledge is the primary research forum on Learning Analytics. The research strand of SocialLearn will be contributing in several ways to LAK12.

  • Simon and Rebecca serve on the executive and steering committees at the newly founded Society for Learning Analytics Research (SoLAR), which oversees the LAK conference, and which is hosting a Summit for educational thought-leaders and funders after LAK. This has excited huge interest from major players, and we’re at capacity now.
  • As part of establishing itself as a new academic discipline, the LAK conference proceedings are in cooperation with the ACM, the world’s largest educational and scientific computing society, and in whose Digital Library all papers are archived (ACM LAK11 Proceedings). LAK therefore has a rigorous peer review process (more so than many other conferences) in which authors and reviewers debate through several iterations, before final decisions are made. So we’re very pleased that three papers made it through.

Ferguson, R. and Buckingham Shum, S. (2012). Social Learning Analytics: Five ApproachesProc. 2nd International Conference on Learning Analytics & Knowledge, (29 Apr-2 May, Vancouver, BC). ACM Press: New York. Eprint:

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.

Buckingham Shum, S. and Deakin Crick, R. (2012). Learning Dispositions and Transferable Competencies: Pedagogy, Modelling and Learning Analytics. Proc. 2nd International Conference on Learning Analytics & Knowledge. (29 Apr-2 May, 2012, Vancouver, BC). ACM Press: New York. Eprint:

Abstract: Theoretical and empirical evidence in the learning sciences substantiates the view that deep engagement in learning is a function of a complex combination of learners’ identities, dispositions, values, attitudes and skills. When these are fragile, learners struggle to achieve their potential in conventional assessments, and critically, are not prepared for the novelty and complexity of the challenges they will meet in the workplace, and the many other spheres of life which require personal qualities such as resilience, critical thinking and collaboration skills. To date, the learning analytics research and development communities have not addressed how these complex concepts can be modelled and analysed, and how more traditional social science data analysis can support and be enhanced by learning analytics.  We report progress in the design and implementation of learning analytics based on a research validated multidimensional construct termed “learning power”. We describe, for the first time, a learning analytics infrastructure for gathering data at scale, managing stakeholder permissions, the range of analytics that it supports from real time summaries to exploratory research, and a particular visual analytic which has been shown to have demonstrable impact on learners. We conclude by summarising the ongoing research and development programme and identifying the challenges of integrating traditional social science research, with learning analytics and modelling.

Haiming, L. Macintyre, R. and Ferguson, R. (2012). Exploring Qualitative Analytics for E-Mentoring Relationships Building in an Online Social Learning Environment. Proc. 2nd International Conference on Learning Analytics & Knowledge, (29 Apr-2 May, Vancouver, BC). ACM Press: New York. Eprint:

Abstract: The language of mentoring has become established within the workplace and has gained ground within education.  As work based education moves online so we see an increased use of what is termed e-mentoring. In this paper we explore some of the challenges of forming and supporting mentoring relationships virtually, and we explore the solutions afforded by online social learning and Web 2.0. Based on a conceptualization of learning network theory derived from the literature and the qualitative learning analytics, we propose that an e-mentoring relationships is mediated by a connection with or through a person or learning objects. We provide an example to illustrate how this might work.

Slides for today’s joint OpenU/UNED Madrid webinar, organised as part of the Open Educational Innovation & Incubation project, as a SCORE Workshop on New Models for Education and Training Built on Open Educational Resources

Key follow-on refs:

Blog post on OpenEd and Drumbeat with seminar replays of these papers:

Buckingham Shum, S. and De Liddo, A. (2010). Collective intelligence for OER sustainability. OpenEd 2010: Seventh Annual Open Education Conference, 2-4 Nov 2010, Barcelona. Eprint:

Buckingham Shum, S. and Ferguson, R. (2010). Towards a social learning space for open educational resources. OpenEd 2010: Seventh Annual Open Education Conference, 2-4 Nov 2010, Barcelona. Eprint:

Learning analytics:

Buckingham Shum, S. and Deakin Crick, R. (2012). Learning Dispositions and Transferable Competencies: Pedagogy, Modelling and Learning Analytics. Proc. 2nd International Conference on Learning Analytics & Knowledge. 29 Apr-2 May, 2012, Vancouver, BC. ACM Press: New York. Eprint: PDF

Buckingham Shum, S. and Ferguson, R. (2011). Social Learning Analytics. Technical Report KMI-11-01, Knowledge Media Institute, The Open University, Milton Keynes, UK.


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