Just over a week ago, the first workshops of the Learning Analytics Knowledge (LAK16) conference got underway at the University of Edinburgh. I’ve been getting up to speed with research in this area for the past few months – so it was a great opportunity to get a close-up look at how the field is evolving and how I might be able to situate my PhD contribution in that bigger context. It was also a wonderful opportunity to meet some people in this dynamic and diverse community of learners and researchers – some of whom I’ve known on Twitter for quite a while. This post is an attempt to capture some thoughts from the event and perhaps work through some questions in subsequent posts.
Sheila McNeil has already written an insightful post comparing the #LAK16 event with Open Education Resources #OER16 – another education conference which took place in the same venue the week before. At first glance, these events seem to be quite different: the emphasis on building a culture of openness at #OER16 feels like quite a contrast to the evidence-based, data-centric feel of #LAK16. It could be argued that one takes the philosophical stance of opening up space, where the other attempts to close down ambiguity and open-endedness in the messy business of learning, with its emphasis on data, measurement and rigor. But after a few days of reflection, I think that’s a simplistic view and I wonder what these two fields can learn from each other.
It was interesting that the two opening keynotes, Catherine Cronin at OER16 and Mireille Hildebrandt at LAK16 both underlined the human aspects of learning – the potential for our humanity to be augmented in new learning spaces as well as the threats to our dignity, liberty and equality. A tweet at LAK16 expressed some incredulity that the term ‘human rights’ would be a used in a discussion about Learning Analytics and I have to say I wondered about this. I look forward to further conversations about this observation.
Learning Analytics, or ‘statistical analysis‘ as David Wiley likes to call it, is an emerging field. For a primer in the domain, I recommend Rebecca Ferguson’s paper from 2012 which sets out an informative historical perspective and identifies some of the sub-domains. We as learners and teachers, are producing ever increasing amounts of data – in Learning Management Systems (LMSs), on social media, in institutional systems. Industry and other fields like healthcare and finance have been using these kinds of data ‘exhausts’ as part of a continuous improvement cycle to increase efficiency, improve performance and augment bottom lines for a long time. So why not do the same for learning? But of course, learning is different.
But shouldn’t we be doing something with all this data? This starting point of: ‘we have data – so let’s do some Learning Analytics’ was critically questioned by Abelardo Pardo (University of Sydney) at the outset of LAK16. He encouraged attendees to ask instead what problems we want to solve in education and how can data and analytics help us to solve them? This prompt really resonates with me: how can we bring our critical faculties to bear on the potential for data to do good in our learning spaces? What are the problems we see? What are the problems that we cannot see? One of the affordances of data is that it makes the invisible and the unquestioned visible. What are the problems we can make visible and solve – this is our question, surely?
Ethics and privacy are key concerns in this field and LAK has a history of giving this topic due consideration and thought. This year was no exception and one of the highlights was a paper by Drachsler & Greller which builds on the sterling work of Prinsloo & Slade at previous LAK conferences and provides a checklist to institutions and individual teachers and researchers to help build and maintain trust in the Learning Analytics process. For me, this question deepened in terms of complexity during the conference – I became more aware of the safeguards we need to ensure learners’ interests are protected – but I also appreciated the opportunities that are opening up to empower students to become more active agents in their own learning journey. As learners, teachers, data scientists and researchers, we have a delicate balance to manage. Err too far on either side, and we will waste these opportunities or do harm.
Student voice was in short supply at LAK12 when Audrey Watters wrote about that event four years ago and I have to report that has not changed significantly. One presentation stood out as an exception here: Jen Tan from Nanyang Technological University, Singapore presented work from a second level institution where young learners were picking up some of the skills of networked learning. Their voice came through loud and clear, describing how their classroom visualisation ‘makes me more motivated to comment so that my [social network] dot can be bigger and brighter’. There are many interesting interpretations of what students are telling us here.
Many worry about the role of the teacher in the classroom of the future, but Aneesha Bakharia and her colleagues at Queensland University of Technology (QUT), Australia put the teacher right at the heart of their mixed methods research. They used qualitative research to dig deeper into the experiences of teachers and their learning design practices and how these related to learning analytics toolsets. It’s a paper I’m looking forward to reading in more detail both from a research question point-of-view and from a methodology standpoint.
Lots more I want to write about – maybe in another post… or two:
- LAK Failathon – talking about failure – we should do more of this
- Erik Duval – a wonderful tribute at LAK16 to one of its founder and most influential members
- CLA Toolkit – bringing learner data together from disparate sources and the #xapi data format
- PELARS Project – wonderful work in Problem Based & Informal Learning spaces
- The Broad Church that is LAK
- The need provide data literacy capability-building opportunities for learners and teachers
- The use of Bayesian Networks in modeling user learning a theme picked up by Robert Mislevy in his keynote
- That Mark Glynn from DCU and I were the only attendees from Ireland was a surprise
And of course, some of the wonderful LAK people I met in Edinburgh – Aneesha Bakharia, Kirsty Kitto, Sheila McNeill, Doug Clow, Martin Hawksey, Garron Hillaire, Daniel Spikol, Tore Hoel, Dragan Gasevic, Vania Dimitrova, Jeff Grann, Shane Dawson.
… to be continued
Ferguson, R. (2012). Learning analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5/6), 304. http://doi.org/10.1504/IJTEL.2012.051816
Drachsler, H., & Greller, W. (2016). Privacy and analytics: it’s a DELICATE issue a checklist for trusted learning analytics (pp. 89–98). ACM Press. http://doi.org/10.1145/2883851.2883893
Buckingham Shum. (2016). #LAK16 Hildebrandt keynote: A world first? #LearningAnalytics uttered in the same breath as “Human Rights Infringements” [Tweet] Retrieved on 05 May 2016 from https://twitter.com/sbuckshum/status/725237955070201856
Cronin, C. (2016). ‘Open Culture, Open Education, Open Questions’ Keynote #OER16 retrieved 05 May 2016 from http://www.slideshare.net/cicronin/open-culture-open-education-open-questions
Hildebrandt, M. (2016). ‘Learning as a machine. Cross-overs between humans and machines’ Keynote #LAK16 retrieved 05 May 2016 from http://lak16.solaresearch.org/?page_id=14#k2
Watters, A. (2012) ‘Learning Analytics: Lots of Education Data… Now What?’ retrieved 05 May 2016 from http://hackeducation.com/2012/05/04/learning-analytics-lak12
Tan, J. P.-L., Yang, S., Koh, E., & Jonathan, C. (2016). Fostering 21st century literacies through a collaborative critical reading and learning analytics environment: user-perceived benefits and problematics (pp. 430–434). ACM Press. http://doi.org/10.1145/2883851.2883965
Bakharia, A., Corrin, L., de Barba, P., Kennedy, G., Gašević, D., Mulder, R., … Lockyer, L. (2016). A conceptual framework linking learning design with learning analytics (pp. 329–338). ACM Press. http://doi.org/10.1145/2883851.2883944