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.

About the author

Simon Buckingham Shum

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