Our Learning Analytics are our Pedagogy

In this keynote to  Macquarie University’s Expanding Horizons 2012 conference , I gave an overview of how the field is developing at macro, meso and micro scales, and then later on make a connection to the seminal work of the Assessment Reform Group on Assessment for Learning, in which University of Bristol played an active role through Patricia Broadfoot, and also to Paul Collard, who presents what is for me a provocative metaphor for the challenge we now face in education: musical reproduction ≠ musicality

Abstract: “Learning Analytics”: unprecedented data sets and live data streams about learners, with computational power to help make sense of it all, and new breeds of staff who can talk predictive models, pedagogy and ethics. This means rather different things to different people: unprecedented opportunity to study, benchmark and improve educational practice, at scales from countries and institutions, to departments, individual teachers and learners. “Benchmarking” may trigger dystopic visions of dumbed down proxies for ‘real teaching and learning’, but an emu response is no good. For educational institutions, our calling is to raise the quality of debate, shape external and internal policy, and engage with the companies and open communities developing the future infrastructure. How we deploy these new tools rests critically on assessment regimes, what can be logged and measured with integrity, and what we think it means to deliver education that equips citizens for a complex, uncertain world.

PhD: Learning Analytics for Learning Power

Learning Analytics for Learning Power
Knowledge Media Institute, The Open University, Milton Keynes, UK
3 year fully-funded PhD (Oct. 2012-Sept.2015), Stipend: £40,770 (£13,590/year)

Supervisors: Simon Buckingham Shum & Rebecca Ferguson, working in collaboration with Ruth Deakin Crick

Full details SocialLearn Research blog

Analytics for lifelong learning

The International Conference on Learning Analytics & Knowledge (LAK) is the primary research forum on Learning Analytics, the rapidly growing community studying how we can make use of ‘Big Data’ (static data sets and live data streams), analytics to make sense of them, and recommendation engines to personalise the learner’s experience. Here are previews of three papers to be presented at LAK2012, which connect in different ways to the core themes of LearningEmergence.net…

This joint OU-Bristol paper sets out how Bristol’s work with ELLI and the Learning Warehouse platform intersects with the emerging learning analytics field, and projects forward to real-time ELLI analytics grounded in online social learning platforms…

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: http://oro.open.ac.uk/32823

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.

A companion paper from the OU sets a broader context for considering ELLI-baed analytics as an example of Disposition Analytics, one of several kinds of Social Learning Analytic:

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: http://oro.open.ac.uk/32910

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.

Thirdly, given the central role that mentoring plays in the use of ELLI, this short paper may also be of interest, which considers online mentoring:

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: PDF

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.

Learning to Learn Analytics

Learning Analytics is a rapidly emerging field, asking the question: Can we discern meaningful learning from the digital ‘vapour trails’ that learners leave behind them? The million dollar question this begs is, of course, what do we mean by “meaningful” learning, and what kinds of learning are important for the 21st Century landscape, whose contours are shifting faster than theory and practice can keep up with?

Here’s the podcast and slides from Learning Analytics: Dream, Nightmare or Fairydust? — my keynote at Ascilite 2011, in which I introduce ELLI as one of the promising signposts to the ways in which we can think about analytics for the new learning paradigms needed to prepare for a complex, uncertain world.

Learning Analytics and Knowledge 2012

Learning Analytics and Knowledge Conference, April 29-May 2, 2012


  • Full Paper submission: October 16, 2011
  • All other submissions: November 13, 2011


  • Katy Börner, George Siemens, Barry Wellman

We are experiencing an unprecedented explosion in the quantity and quality of information available not only to us, but about us. We must adapt individually, institutionally and culturally to the transition in technologies and social norms that makes this possible, and question their impacts. What are the implications of such data availability for learning and knowledge building — not only in established contexts, but also in the emerging landscape of free, open, social learning online?

This conference will be of interest to Learning Emergence readers since we are unquestionably entering the era of data mining, in which machines will be tasked with helping over-pressed humans to make sense of the data deluge. When this comes to learning, we need to make sure that the richness of authentic, connected learning is not lost through over-simplified indicators of “learning” which are deployed simply because they are the easiest things to formalize.

Full details of the topics, keynotes, and ways to participate on the website