Discussion, collaborative knowledge work and epistemic fluency


I received a request for this paper earlier today. It started life as a keynote at the Networked Learning conference in Lancaster in 2006. Maria Zenios visited us in Sydney later that year, and we were able to work together and develop a more extensive treatment of the issues. We used a recent paper in BJES by Effie MacLellan as a springboard. We combined ideas from Stellan Ohlsson, Allan Collins, Dave Perkins and Carl Bereiter to introduce epistemic tasks, forms, games and fluency. Then we linked this with research on learning through discussion by Helen Askell-Williams and Michael Lawson and by Rob Ellis and myself, to distinguish between weaker and stronger forms of collaborative knowledge building. If you’re serious about helping students prepare for work in complex knowledge creating jobs, then you need the stronger form.

I hadn’t reread this paper for a while, and I think it still stands up quite well. As of today, it’s had 87 citations, not all of them by me. I’m also glad to see that research on learning through discussion in higher education has been growing in the last 10 years. The literature was quite thin in 2006/7.

In 2008, Lina Markauskaite and I wrote a grant proposal that allowed us to do some of the ‘cognitive anthropology’ hinted at in this paper. The outcomes, and a much richer understanding of matters that were only sketched in the BJES paper, can be found in our ‘magnum opus’ – Markauskaite, L., & Goodyear, P. (2017). Epistemic fluency and professional education: innovation, knowledgeable action and actionable knowledge. Dordrecht: Springer.


Slides from my invited lecture at ascilite

ascilite title slide

This may not be the final version, but will have most of the references, ideas etc.


Analysis and design for complex learning …

Over the last year or so, there has been a good deal of online soul-searching about the field or discipline of educational technology: about its nature, foundations, scope and purpose – including whether and how it can make a difference to policy and practice in higher education, which is ascilite’s home ground. In this talk, I want to focus on the production of educational design knowledge: knowledge that is useful to people who design for other people’s learning. I will use, as an illustrative example, the ACAD framework – an Activity-Centred approach to Analysis and Design – to make some points about the creation of useful design knowledge. In so doing, I hope to (a) draw attention to a family of approaches to research and development that are particularly well-suited to understanding and improving complex learning systems through local action, and (b) explain why analysis and design processes involve epistemic fluency (an ability to work with different kinds of knowledge and ways of knowing). The talk should be of interest to anyone who is concerned about connecting inquiry and action in educational technology.

Understanding the nature and impact of wicked problems and unpredictable futures on employability

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September 2016 – a brief presentation at Joy Higgs’s EPEN seminar (Education, Practice and Employability Network). Videos here.

The Kilpi quote is from

Kilpi, Esko. (2016). Perspectives on new work: exploring emerging conceptualizations. Retrieved from: http://www.sitra.fi/en/julkaisu/2016/perspectives-new-work-1

Activity centred analysis and design in the evolution of learning networks

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A talk at the 10th International Conference on Networked Learning at Lancaster University in the UK (May 2016).


This paper provides an overview of, and rationale for, an approach to analysing complex learning networks. The approach involves a strong commitment to providing knowledge which is useful for design and it gives a prime place to the activity of those involved in networked learning. Hence the framework that we are offering is known as “Activity Centred Analysis and Design” or ACAD for short. We have used the ACAD framework in the analysis of 20 or so learning networks. These networks have varied in purpose, scale and complexity and the experience we have gained in trying to understand how these networks function has helped us improve the ACAD framework. This paper shares some of the outcomes of that experience and describes some significant new refinements to how we understand the framework. While the framework is able to deal with a very wide range of learning situations, in this paper we look more closely at some issues which are of particular importance in networked learning. For example, we discuss the distributed nature of design in networked learning – acknowledging the fact that learning networks are almost invariably co-configured by everyone who participates in them, and that this aspect of participation is often explicitly valued and encouraged. We see participation in (re)design as a challenging activity: one that benefits from some structured methods and ways of representing and unpicking the tangles of tasks, activities, tools, places and people

Here’s a pdf of the paper, which is also freely available online as part of the conference proceedings. Cite as: Goodyear, P., & Carvalho, L. (2016). Activity centred analysis and design in the evolution of learning networks. Proceedings of the 10th International Conference on Networked Learning, Edited by: Cranmer S, Dohn NB, de Laat M, Ryberg T & Sime, JA. Pp218-225. (ISBN 978-1-86220-324-2) http://www.networkedlearningconference.org.uk/abstracts/pdf/P16.pdf

And a copy of the slides, though not all were used in the presentation.


Roberto presents our Design studio paper at CHI

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May 2016: Roberto Martinez-Maldonado presented our work at the CHI conference in San Jose.

Abstract: There is a steadily growing interest in the design of spaces in which multiple interactive surfaces are present and, in turn, in understanding their role in group activity. However, authentic activities in these multi-surface spaces can be complex. Groups commonly use digital and non-digital artefacts, tools and resources, in varied ways depending on their specific social and epistemic goals. Thus, designing for collaboration in such spaces can be very challenging. Importantly, there is still a lack of agreement on how to approach the analysis of groups’ experiences in these heterogeneous spaces. This paper presents an actionable approach that aims to address the complexity of understanding multi-user multi-surface systems. We provide a structure for applying different analytical tools in terms of four closely related dimensions of user activity: the setting, the tasks, the people and the runtime co-configuration. The applicability of our approach is illustrated with six types of analysis of group activity in a multi-surface design studio.

Further information and video on the CHAI website.

Learning: Research and Practice

It’s not often I get excited by the arrival of a new journal, but this one is making a terrific start and the papers in the first issue are currently on open access. In issue 1 we have: Mitchell Nathan on gesturing in mental model construction (drawing on research in embodied cognition); Wolff-Michael Roth on a post-constructivist theory of learning; Manu Kapur on productive failure; Deanna Kuhn on argumentation as core curriculum and Alexander Renkl on principles-based cognitive skills.

Learning: Research and Practice is an initiative of the National Institute of Education in Singapore. The journal has been under development for quite some while – great to see the first issue now.

Learning research & practice cover

Brain and mind

I’ve been working on what we hope will be the final drafts of the Epistemic Fluency book.  I needed to check something Stellan Ohlsson wrote a few years ago, and come across this perfect pearl:

“Other researchers deny mind by equating it with the brain. This approach is the more attractive, the less a person knows about psychology. It is a perennial favorite among computer scientists, members of the medical profession and particle physicists with philosophical aspirations. … Neuroscience is a fascinating science and it has made great progress in explaining the workings of the brain, a most worthwhile goal. No value is added to this science by the unwarranted metaphysical claim that a complete description of the brain will answer all questions about mind.”

Ohlsson, S. (2011). Deep learning: how the mind overrides experience. Cambridge: Cambridge University Press. p26

There’s much much more to the science of learning …

My smarter instincts tell me to shut up. This will blow over much more quickly if nobody fans the flames. As a rabid young academic I wouldn’t have hesitated to go into battle against outrageous claims and muddle-headed arguments. But the 80s were more turbulent, angrier times in which many academics put the truth (as they saw it) and their ego ahead of politeness and collegiality.

Yesterday’s Australian ran an article under the following headline: UQ’s Pankaj Sah says rigorous trials could revolutionise education”. If it’s not hidden behind Rupert’s paywall, you can find the text here.

Pankaj Sah runs the Science of Learning Research Centre at UQ (University of Queensland) and has recently been appointed as editor-in-chief of a new Nature journal: npj Science of Learning. There are press releases from Nature and UQ to announce the event. If you can’t get behind the paywall then the gist of the story is in the press release.

At this point I should declare an interest and say that 10 years ago I helped set up, and have since been co-directing, Australia’s largest and most successful centre for research in the learning sciences. In 2012-13, with an extraordinarily talented team of researchers from Sydney, Monash, Curtin, Griffith, Charles Sturt, Berkeley and London, we bid in the competition for ARC funding that UQ won. We lost. It happens.

Sharp-eyed readers will have noticed that UQ runs a “Science of Learning Centre” but I described our centre as researching in “the learning sciences”. These phrases – science of learning, learning sciences – have become increasingly popular since the early 90s when Roger Schank, Roy Pea and others set up the Learning Sciences program at Northwestern. There’s been a Journal of the Learning Sciences for nearly 25 years now and there’s a biennial international conference, which we had the honour of hosting in Sydney in 2012. The second edition of the Cambridge Handbook of the Learning Sciences came out a few months ago. Both it, and the very successful 2006 first edition, provide an excellent overview of the field. A few of the centres overseas that specialize in this area call themselves “Science of Learning”; most opt for “Learning Sciences” (which is what the Australian Bureau of Statistics calls this field: FoR 130309 Learning Sciences).

So is there a difference between the “Science of Learning” and the “Learning Sciences” and should anyone care? Outside Oz, I’d say the two terms mean pretty much the same thing. But UQ and Nature seem intent on forcing a difference. And I think this spells trouble.

npj Science of Learning says it will publish: “peer reviewed research into the neurobiology of learning in experimental conditions and in an educational environment”. SoL = neurobiology of learning. In the Australian article and in the UQ press release, SoL appears to mean a combination of neuroscience and controlled trials.

The learning sciences have become a strong and vibrant area because they (a) recognize the complex array of influences on learning (“from neurons to neighborhoods” is the well-polished phrase) and (b) are developing methods that can connect an understanding of complex, emergent phenomena to the design and management of real learning environments. Controlled trials can be part of this story, but we’ve known for some years that what they can tell us about how to assist human learning in the real world is very limited. Some of the reasons for this are well known, and ought to be familiar to anyone running a Science of Learning centre.

In the Australian article, analogies are made with medicine and drug trialing (and of course Rupert’s flagship paper can’t help but talk about “educational fads”, to get the green ink brigade foaming at the mouth…). Here’s the thing:

  1. Yes, a lot of what gets tried in classrooms is not underpinned by good evidence.
  2. There is no good evidence to believe that significant numbers of teachers will read npj Science of Learning: it would be unscientific to assume that they will, or to assume that reading the outcomes of research on the neurobiology of learning will help them shift to evidence-based teaching in their classrooms.
  3. Copying Medicine does NOT mean jumping to the educational equivalent of large-scale, controlled trialing of drugs. Medicine is itself moving away from a “one best drug suits all cases” paradigm, to a more personalized approach in which deep knowledge of a host of patient attributes is needed to optimize treatment. Moreover, there’s much more to recovery and staying well than getting the right drug. How well a person understands their own health, medical conditions, treatments, diet, etc., and how far they are able to act on such knowledge, are keys to well-being. Medicine is learning from Education too.
  4. Moreover, Medicine doesn’t jump straight to large-scale trialing. Medical research involves subtle work on fundamental physiological and other mechanisms – there is a long, complicated path between bench and bedside, and a growing recognition of the need for special processes and personnel to do the translational work.
  5. Just like Medicine, Education needs to get better at translational work. It needs to invent new processes, and develop new specialist jobs, to help translate research evidence into action-oriented knowledge: for teachers, students, parents and journalists. Translational work of this kind needs to fill the void between basic research on mind, brain and learning and sustainable educational improvement. Understanding how to do this is not trivial – it needs research in its own right, and that research is scientific (orderly, well-grounded). Indeed, one might see it as at the core of most research in the learning sciences.

Professor Sah is a distinguished researcher who has developed a profound understanding of the complexities of the physiology of the amygdala and its role in the processing of emotions. In the Australian, he is quoted as saying:

“In animal models, patterns of reinforcement make a pretty big difference. These are things neuro­science has a lot to say about, and it has not really trickled into education at all.”

It ought not to be possible to make such a statement without reference to B. F. Skinner, Behaviourism and Programmed Instruction. Understanding the rise and fall of Skinner’s enterprise, and of other hubristic ventures, is in the DNA of most learning scientists, one might say.

“Sah says that in five or 10 years educationalists could dismiss the approach as harebrained.”

My worry: apart from the loony few who are seduced by the neuromyths, educationalists and educators are dismissing it already. Part of the challenge of the learning sciences is to take a scientific approach to understanding why this is so – and developing, credible, evidence-based strategies for doing something about it. Now.

(By the way, I’m well aware that a significant number of projects being run under the auspices of the ARC Science of Learning Centre are not narrowly neuro. Many of them are just the thing one would expect to find in the portfolio of a broad-based learning sciences research centre anywhere in the world. The conundrum, for me, is why a Science of Learning research centre would run a Science of Learning journal that uses such a narrow definition of the Science of Learning that it would exclude many of the publications emanating from the Centre’s own projects.)

Photo credit: Tim Parker