SocialLearn Research Blog

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

Background

Intrinsic motivation to engage in learning (whether formal/informal, or academic/workplace) is known to be a function of a learner’s dispositions towards learning. When these are fragile, learners often disengage when challenged, and are passive, lacking vital personal qualities such as resilience, curiosity and creativity needed to thrive in today’s complex world. Learning Analytics seek to improve learning by making the optimal use of the growing amounts of data that are becoming available to, and about, learners [1,2]. Dispositional Learning Analytics seek specifically to build stronger learning dispositions (note that these are not ‘learning styles’, which have a dubious conceptual basis [3]). One particularly promising approach models dispositions as a 7-dimensional construct called Learning Power, measured through self-report data [4]. A web application generates real time personal and cohort analytics, which have been shown to impact learners, educators, and organizational leaders, and the underlying platform pools data from >50,000 profiles, which in combination with other datasets, enables deeper analytics. As a form of Social Learning Analytic [5], in combination with Discourse-Centric Analytics [6-7] and Social Network Analytics for learning [8], our strategic goal is to provide a suite of analytics that can help learners grow in Learning Power, and ultimately, build their capacity as life-long, life-wide learners.

PhD Challenge

A key next step in this research programme is to harvest and analyse data from the traces that learners leave as they engage in social digital spaces, and to explore its relationship to other data sets and data streams. This PhD will therefore fund a technically strong candidate to design, implement and evaluate “Learning Analytics for Learning Power”. The project will deploy iterative prototypes in authentic use contexts, considering OU platforms as a starting point (e.g. SocialLearn [9]; Cohere [10]; EnquiryBlogger [11]), but open to data streams from new kinds of digitally instrumented interactions (e.g. from Pervasive Computing; Augmented Reality; Quantified Self). A possible outcome is an analytics architecture open to diverse forms of input, grounded in a theoretically robust framework, generating visual analytics with transformative power for reflective learners.

Seize the Day!

This is a fantastic opportunity if you’re passionate about the future of learning and want to engage with next generation pedagogy. You’re already a great web and database developer, and now looking to develop a research career in Learning Analytics, one of the fastest growing topics in technology-enhanced learning. Your supervisors will be Simon Buckingham Shum and Rebecca Ferguson, who are active contributors to the field of Learning Analytics research. The PhD will be in collaboration with learning scientists and practitioners in the global LearningEmergence.net research network, who are well placed to provide pedagogical input, user communities and impact evaluations. In particular, this work will be done in partnership with Ruth Deakin Crick, University of Bristol (Graduate School of Education & Centre for Systems Learning & Leadership), whose research into modelling and assessing learning power provides a foundational element for this project.

Open U. is the place to do research in learning technology, this being an institutional mission-critical challenge. The Knowledge Media Institute is an 80-strong state of the art research lab, prototyping the future for the Open University and the many other organisations with whom KMi partners. KMi is renowned for its creative, can-do culture, and its high impact on the OU’s strategic thinking and technical capacity [Locate KMi]. The Institute of Educational Technology (where Rebecca Ferguson is based) is home to world leading research on learning technology, as well as conducting institutional research to inform the university’s core business. You will be part of a comprehensive doctoral training programme in computing and educational technology, participating in a dynamic research community, with opportunities to connect with groundbreaking people and ideas — limited only by your energy and imagination.

To Apply…

• Contact us if you have informal queries. We’re in Vancouver next week at the 2nd International Conference on Learning Analytics & Knowledge, or get in touch online for informal enquiries: s.buckingham.shum or r.m.ferguson atsign open.ac.uk.
• Read the background papers, and research technical approaches which will enable you to locate your ideas within the literature on learning analytics. See for instance the ACM Learning Analytics Conference (2011/2012), and SoLAR resources, and other research conferences around data mining and visualization.
• Open U. Research website gives an overview of the quality of research conducted here, and the Research Degrees Handbook which will answer many practical questions
• Draft a proposal outlining how you might tackle this challenge, highlighting which of the above approaches you might focus on, and the skills/experience that you bring (plus any that you recognise you will need to acquire). 4 pages max. This is not a binding document, but shows us how clearly you can think and write.
• Complete the PhD application form [Word doc]
• Email these with your CV and a cover letter, cc’ing Simon Buckingham Shum & Rebecca Ferguson, to: Ortenz Rose <O.Rose@open.ac.uk>
• Deadline 7 June: You will be competing for one of several studentships being offered across all KMi research topics.

Reading

1. 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. http://kmi.open.ac.uk/publications/techreport/kmi-12-01
2. Buckingham Shum, S. (2011). Learning Analytics: Dream, Nightmare or Fairydust? Keynote Address, Ascilite 2011: http://bit.ly/tqedko followed by a Networked Learning Conference 2012 online discussion: http://bit.ly/AfhqEv
3. Coffield, F., Moseley, D., Hall, E. and Ecclestone, K. (2004). Should We Be Using Learning Styles? What Research Has To Say to Practice. London: Learning and Skills Research Centre, 1540/05/04/500. Eprint: http://www.arasite.org/RMdatabase/Coffield.pdf
4. Buckingham Shum, S. and Deakin Crick, R. (2012). Learning Dispositions and Transferable Competencies: Pedagogy, Modelling and Learning Analytics. Proc. 2nd Int. Conf. Learning Analytics & Knowledge. (29 Apr-2 May, 2012, Vancouver, BC). ACM Press: New York. Eprint: http://oro.open.ac.uk/32823
5. Ferguson, R. and Buckingham Shum, S. (2012). Social Learning Analytics: Five Approaches. Proc. 2nd Int. Conf. Learning Analytics & Knowledge, (29 Apr-2 May, Vancouver, BC). ACM Press: New York. Eprint: http://oro.open.ac.uk/32910
6. De Liddo, A., Buckingham Shum, S., Quinto, I., Bachler, M. and Cannavacciuolo, L. (2011). Discourse-Centric Learning Analytics. Proc. 1st Int. Conf. Learning Analytics & Knowledge. Feb. 27-Mar 1, 2011, Banff. Eprint: http://oro.open.ac.uk/25829
7. Ferguson, R. and Buckingham Shum, S. (2011). Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat. Proc. 1st Int. Conf. Learning Analytics & Knowledge. Feb. 27-Mar 1, 2011, Banff. Eprint: http://oro.open.ac.uk/28955
8. Haythornthwaite, C. and de Laat, M. (2010). Social networks and learning networks: using social network perspectives to understand social learning. In: 7th International Conference on Networked Learning (Aalborg, Denmark, 3-4 May 2010). Eprint: http://celstec.org.uk/system/files/file/conference_proceedings/NLC2010_Proceedings/abstracts/PDFs/Haythornwaite.pdf
9. 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: http://oro.open.ac.uk/33457
10. De Liddo, Anna; Sándor, Ágnes and Buckingham Shum, Simon (2012, In Press). Contested Collective Intelligence: Rationale, Technologies, and a Human-Machine Annotation Study. Computer Supported Cooperative Work (CSCW). Eprint: http://oro.open.ac.uk/31052
11. Ferguson, R., Buckingham Shum, S. and Deakin Crick, R. (2011). EnquiryBlogger: using widgets to support awareness and reflection in a PLE Setting. 1st Workshop on Awareness and Reflection in Personal Learning Environments. PLE Conference 2011, 11-13 July, Southampton, UK. Eprint: http://oro.open.ac.uk/30598

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.

• On the organisational front, Simon Buckingham Shum is Programme Co-Chair, and also co-leading a workshop on Connecting Levels and Methods of Analysis in Networked Learning Communities. As Workshops Chair, Rebecca Ferguson has oversight of the workshops programme, new to LAK this year.

• 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 Approaches. Proc. 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.

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.

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: http://oro.open.ac.uk/33632

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.

This morning on the opening day of the CALRG 2011 Conference, we presented some of the recent thinking we’ve been doing on learning analytics, specifically in a social learning context.

A technical report setting out the line of argument in more detail…

Buckingham Shum, S. and Ferguson, R. (2011). Social Learning Analytics. Available as: Technical Report KMI-11-01, Knowledge Media Institute, The Open University, UK. http://kmi.open.ac.uk/publications/pdf/kmi-11-01.pdf

Abstract: We propose that the design and implementation of effective Social Learning Analytics presents significant challenges and opportunities for both research and enterprise, in three important respects. The first is the challenge of implementing analytics that have pedagogical and ethical integrity, in a context where power and control over data is now of primary importance. The second challenge is that the educational landscape is extraordinarily turbulent at present, in no small part due to technological drivers. Online social learning is emerging as a significant phenomenon for a variety of reasons, which we review, in order to motivate the concept of social learning, and ways of conceiving social learning environments as distinct from other social platforms. This sets the context for the third challenge, namely, to understand different types of Social Learning Analytic, each of which has specific technical and pedagogical challenges. We propose an initial taxonomy of five types. We conclude by considering potential futures for Social Learning Analytics, if the drivers and trends reviewed continue, and the prospect of solutions to some of the concerns that institution-centric learning analytics may provoke.

I recently  attended Alpine Rendez-Vous 2011, meeting people from all over Europe with an interest in technology-enhanced learning (TEL). For most of the time, we were split into eight workshops – my group was exploring ‘Methods and models of next-generation technology enhanced learning‘, examining the roles of assessment and evaluation in learning.

A focus for the workshop was to identify the ‘Grand Challenges’ for future research – constructing a framework for the development of TEL over the next ten years. The ideas were wide-ranging and exciting, for example:  Develop new technology to harness the power of emotions for learning. One challenge was closely related to our work at SocialLearn on learning analytics and recommendations engines: ‘How can learning be assessed in an open, social TEL environment?’

Our current model for the assessment of learning is primarily summative and individual, firmly bound to hierarchical education structures. It is a model that was developed for use when educational technology had only just moved beyond the horn book, knowledge was not abundantly available, groups of learners were taught and examined at the same time in the same physical location, teachers and learners were clearly differentiated and online collaboration and publication were undreamed-of possibilities. As new models of learning have been widely adopted, this model of assessment is no longer fit for purpose. We need new approaches and new models.

Open, social TEL environments have made new models of learning possible. Learners now draw upon many different people and resources, knowledge is dispersed and distributed, individuals may move rapidly between the roles of teacher and learner, and their collaborations extend across time and space. Despite these changes, the group work, the growing archives of activity and the available data about learning networks, there is still a focus on summative assessment of individuals.

TEL environments offer learners and educators a wealth of new resources in the form of the data they record – learners’ demographics, activities, interactions, participation and engagement – but little of this is currently harnessed to support assessment and even less is used to provide formative feedback and to help learners develop their metacognitive skills, their learning dialogue, their skill-sets or their knowledge.

Colleges and universities are beginning to make use of this data, but educational data mining and analytics are often viewed from an institutional point of view – how can we recruit more students? how can we retain students for longer? how can this institution maximise its income? A wordsearch for ‘teaching’ or ‘learning’ in the current literature produces woefully limited results.

Our challenge is to make use of new resources and technologies to develop and build on learning analytics – analytics that can make a real difference for learners, producing measurable improvements in areas such as:

• Engagement with learning – supported by appropriate and personalised feedback
• Quality of online learning dialogue
• Engagement with online learning networks
• Enjoyment – due, in part, to development of a students-in-trouble alerting system
• Learners’ and teachers’ awareness of the value of learning analytics and recommendations.