Author: petergoodyear

The Art and Science of Learning Design

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There’s a chapter from the work of our ARC Laureate team featuring in this new book. Really pleased with it.

Goodyear, P., Thompson, K., Ashe, D., Pinto, A., Carvalho, L., Parisio, M., . . . Yeoman, P. (In press, 2015). Analysing the structural properties of learning networks: architectural insights into buildable forms. In B. Craft, Y. Mor & M. Maina (Eds.), The art and science of learning design (pp. 15-29). Rotterdam: Sense.

Here’s the Overview

OVERVIEW

A good repertoire of methods for analysing and sharing ideas about existing designs can make a useful contribution to improving the quality and efficiency of educational design work. Just as architects can improve their practice by studying historic and contemporary buildings, so people who design to help people learn can get better at what they do by understanding the designs of others. Moreover, new design work often has to complement existing provision, so the sensitive analysis of what already exists is an essential part of enhancing, rather than undermining, prior work (Goodyear & Dimitridis, 2013). Since many factors can affect what and how people learn, the scope of analysis for design is broad. In fact, it has to go beyond what has been explicitly designed for learning, to take into account the various configurations of things, places, tasks, activities and people that influence learning. Part of the skill of analysis is knowing how to put a boundary on what one studies (Hutchins, 2010). We believe that analysis of this kind can help improve the design of all kinds of technology-enhanced learning (TEL) systems. But to focus our argument, this chapter draws on our recent collaborative analyses of learning networks (Carvalho & Goodyear, 2014). Our thinking has been influenced quite strongly by the writings of Christopher Alexander on the properties that ‘give life’ to places and artefacts. The first part of the chapter has an ontological function – since analysis involves some decisions about the nature of the existence of its objects of inquiry. The second part illustrates the application of some of Alexander’s ideas to the analysis of the structural properties of learning networks, where the goal of analysis is to inform design.

TELS-Craft_PB.indd

Brain and mind

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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

Book review: The Design, Experience and Practice of Networked Learning

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Review of Hodgson, V, de Laat, M, McConnell, D, & Ryberg, T. (eds) The Design, Experience and Practice of Networked Learning
Springer, Heidelberg, 2014.
DOI: 10.1007/978-3-319-01940-6

Note: this is my pre-publication version of this invited review, which appeared in the journal Technology, Knowledge & Learning in Jan 2015. (http://dx.doi.org/10.1007/s10758-014-9243-3)

The review is structured according to standard sub-headings used for book reviews in the TKL journal. Unfortunately, the published version of the review omits the final word of the book’s title. The journal published an erratum to this effect (http://dx.doi.org/10.1007/s10758-015-9247-7 ). To help make up for that, I’m posting the pre-publication text here too.

Author(s) and Contents of the Book

This book draws together a selection of outstanding papers from the 8th biennial Networked Learning Conference, held in 2012 in Maastricht, The Netherlands. The editors – Vivien Hodgson, Maarten de Laat, David McConnell, and Thomas Ryberg – are well known, within Europe and more widely, as leading figures in the field of research and development that has emerged in the last 15 years or so under the name ‘networked learning’. Indeed, Hodgson and McConnell are among the originators of this field, which focuses on researching situations in which people learn in collaboration with others, with much or all of their interaction being mediated through network technologies (Steeples & Jones, 2002; Goodyear, 2014).

Conference proceedings can be of uneven quality and sometimes offer an unsatisfactory sampling of contemporary work. This is not the case here. The editors have been able to select from among strong papers, which authors have revised in light of feedback at the conference itself and from the editors. Overall, this makes for a good representation of the field, with contributions from a number of its best-known researchers and educational innovators. The coherence of the book emerges from several sources, in addition to the editors’ judgement and commentary. This is quite a tight-knit area of work, with substantial overlaps of interest, not just in substantive issues but also in terms of theoretical perspectives and pedagogy.

The book contains 12 main chapters preceded by an editors’ introduction. They are grouped into three sections: “Networked Learning Spaces and Context: Design and Practice”, “Networked Learning in Practice: The Expected and Unexpected” and “The Practice of Informal Networked Learning”. It should come as no surprise to hear that many of the chapters take a practice-based view of learning: there is not much here that reflects cognitive psychological perspectives. Moreover, the book includes a number of contributions that are influenced by socio-material theorizing, with more than a smattering of Actor Network Theory. It would be unfair to some of the authors to try to capture the flavour of the book in a single sentence, but the essential elements that come together here are: learning as engaging with others in substantial projects of shared concern, drawing together and held together by complex assemblages of people, digital and material tools and other artefacts. Teachers’ work then becomes a matter of designing for such learning, guiding and facilitating some of the activities that unfold, and promoting reflection on the shared experiences and their outcomes. Dewey, Illich, Freire and Schön meet Latour, Schatzki, Hutchins and Orlikowski.

This book will, of course, be read by researchers who already identify with the Networked Learning conferences and with the spin-off book series in which this volume sits (see also Dirckinck-Holmfeld et al., 2009, 2012; Goodyear et al., 2004). It should also be required reading for other researchers who are serious about the broader field of computer-supported collaborative learning (CSCL). One can still see the joins between European and North American traditions of research in CSCL. The latter still appears to be mainly concerned with small group learning, by school-aged students, in formal educational settings, using framings that mainly derive from educational psychology and/or sociolinguistics. The European tradition in CSCL is more diverse, with a wider range of interests, drawing on a richer set of theoretical and methodological resources. This book sits closer to that tradition: indeed there is some overlap in personnel between the networked learning and European CSCL research communities, and naturally enough some shared interests in adult learning, informal learning, practice theory and socio-material studies.

For example, the last four chapters in the book are all concerned with networked learning to which the label ‘informal’ can be applied. Two are concerned with continuing professional development, one with investigating the ego-networks of school age children and one with serious hobbyists. Most intriguing of these is Steve Wright and Gale Parchoma’s chapter, based on Wright’s PhD research, which uses Actor Network Theory (ANT) in a careful tracing of the network of interacting entities (human and not) implicated in the practices of home-brewing.

“we follow non-human actors, tracing the movements of recipes through two different breweries, enmeshing and assembling a network of actants including a brewer, brewery equipment, an iPhone, apps, podcasts, YouTube videos, grains, yeast, hops and more.” (Wright & Parchoma, 2014, p247)

The authors acknowledge Steve Fox’s seminal contribution to the use of ANT in networked learning (e.g. Fox, 2002) and take this much further by giving us an exemplary illustration of the power of ANT in helping identify how people assemble and hold together learning networks. In formal education, it can appear that learning networks are defined by educational providers – as if they can be brought into being by fiat. Wright & Parchoma’s example shows how ‘informal’ learning networks need to be mapped empirically. The additional step that needs to be taken is to assert that all learning networks need this kind of scrutiny – the network which is defined by the educators organizing network learning is usually only a part of the network experienced by participants, whose learning activities and other life projects extend the network into areas that may be invisible to the providing educators (Goodyear & Carvalho, 2014).

In many of the early writings in what has become the networked learning field, there was a strong focus on online facilitation – how to provide guidance in online discussions, for example (Mason & Kaye, 1989; McConnell, 1994; Salmon, 2000). More recently, there has been something of a swing towards design, with less attention, relatively speaking, being paid to understanding the tutor’s role. So the chapter by Hilary Periton & Mike Reynolds, in Part II of the book, is a welcome corrective. Their focus is on ways in which networked learning tutors can collectively tackle emerging difficulties in online groupwork. They don’t offer tips and tactics. Much better than this, they offer some productive ways for teachers to think about, and discuss among themselves, the dilemmas that it is sensible to expect in collaborative learning. They do this using an imaginative methodology – one in which a group of experienced networked learning teachers react to a fictionalized online discussion among students. Anyone with teaching experience in this field will smile with recognition at the contradictions that emerge between our idealized and embodied ‘pedagogical selves’.

The final chapter I want to pick out in this review is by Janne Gleerup, Simon Heilesen, Niels Henrik Helms and Kevin Mogensen, in Part I of the book. It is concerned with the use of networked learning approaches in the education of apprentices, with the express goal of strengthening connections between learning and work. Gleerup et al (2014) can be recommended as a compelling example of how to analyse needs and design for vocational learning in a way that reflects the best of what we know about learning in practice, learning with others, and learning as authentic engagement in innovation. It builds on activity theory and the Scandinavian tradition of participatory design. It offers a compelling picture of design for learning as a learning activity.

I have picked out a favourite chapter from each of the three parts in the book. There is much more to be enjoyed in this collection. Nina Bonderup Dohn offers a beautifully constructed lesson to those among us who are not so careful in our use of theory – pointing out how ‘practice’ is an ill-used construct in much of the networked learning literature, and how we are prone to favour explicit over tacit knowledge, and to work with very hazy images of how learning matters in our students’ lives. Bart Rienties and colleagues give us a nice analysis of ‘knowledge spillovers’ in networked learning, showing how a construct from regional economic development, combined with empirical research using network analysis tools, can illuminate some valued learning processes: collateral benefit, when it works well.

Evaluation

It should be clear by now that I like this engaging, useful book. Its contributions are well-written and well-judged. They are cautiously positive, which I find refreshing when contrasted with the self-promoting salesmanship or jaded critical analysis of much of what one finds in today’s ed. tech. literature.

That said, I think it would be fair to make the following remarks, particularly as there are likely to be more books in this series, and book editors and conference organisers have a responsibility to offer some leadership to a dynamic field.

First, it is clearly time to make a rapprochement with psychologically-informed accounts of human action, competence and learning. Some of the most interesting research and writing about these complex phenomena is drawing on recent developments in grounded and distributed cognition as well as on ideas about materials and materiality (e.g. Clark, 2003, 2008; Clark & Chalmers, 1998; Hodder, 2012; Hutchins, 2010; Ingold, 2011, 2013; Kirsh, 2013; Malafouris, 2013; Malfouris & Renfrew, 2010).

Among these insightful thinkers, Tim Ingold is particularly insistent on the need to take materials seriously. So my second, future-oriented, point is that networked learning researchers should be taking a few more gambles about the likely nature of the tools and artefacts that will be bound up in networked learning in the next decade or so. There has been too much (premature) fuss about the ‘the internet of things’, but we do need some strategies to ensure our research methods and problems aren’t locked to technologies that were new in the 1980s. David Kirsh puts this very nicely, if provocatively, writing about the ‘magical future of interaction design’:

“Good design needs good science fiction; and good science fiction needs good cognitive science” (Kirsh, 2013, p2)

The surge of interest in materiality across the social and human sciences has not been accompanied by (a) a proper interest in the importantly different qualities of materials (Ingold, 2007), or (b) convincing treatment of the qualities of complex digital-material objects in human activity (Faulkner & Runde, 2011). Archaeologists and anthropologists have been developing some useful ways of framing relations between tools, human cognition and collaboration, but (understandably) have not paid much attention to the digital (Malafouris, 2013, Sterelny, 2012). Some of the French ergonomists have been finding interesting ways to trace the coupled evolution of (digital) tools and human capabilities, but are not much interested in education (Rabardel & Bourmaud, 2003; Lonchamp, 2012; Ritella & Hakkarainen, 2012).

An obvious opportunity for networked learning researchers is to make some significant contributions to this space – for once, adding to theoretical developments in the human/social sciences and not merely drawing on them.

Summary Statement

This is an invaluable overview of research in the field of networked learning. It’s a very accessible introduction to the mix of theory, pedagogy and experimental practice that characterises this field. I recommend it highly to all researchers interested in contemporary developments in educational technology, collaborative learning, and the entanglement of digital tools and resources in human activity.

References

Clark, A. (2003). Natural-born cyborgs: minds, technologies, and the future of human intelligence. Oxford: Oxford University Press.

Clark, A. (2008). Supersizing the Mind: Embodiment, Action, and Cognitive Extension. Oxford: Oxford University Press.

Clark, A., & Chalmers, D. (1998). The extended mind. Analysis, 58(1), 7-19.

Dirckinck-Holmfeld, L., Hodgson, V., & McConnell, D. (Eds.). (2012). Exploring the theory, pedagogy and practice of networked learning. Dordrecht: Springer.

Dirckinck-Holmfeld, L., Jones, C., & Lindström, B. (Eds.). (2009). Analysing networked learning practices in higher education and continuing professional development. Rotterdam: Sense Publishers.

Faulkner, P., & Runde, J. (2011). The social, the material, and the ontology of non-material technological objects. Paper presented at the 27th EGOS (European Group for Organizational Studies) Colloquium, Gothenburg. http://webfirstlive.lse.ac.uk/management/documents/Non-MaterialTechnologicalObjects.pdf

Fox, S. (2002). Studying networked learning: Some implications from socially situated learning theory and actor-network theory. In C. Steeples & C. Jones (Eds.), Networked learning: perspectives and issues (pp. 77-92). London: Springer.

Gleerup, J., Heilesen, S., Helms, N. H., & Mogensen, K. (2014). Designing for learning in coupled contexts. In V. Hodgson, M. de Laat, D. McConnell & T. Ryberg (Eds.), The Design, Experience and Practice of Networked Learning (pp. 51-66). Heidelberg: Springer.

Goodyear, P. (2014). Productive learning networks: the evolution of research and practice. In L. Carvalho & P. Goodyear (Eds.), The architecture of productive learning networks. New York: Routledge.

Goodyear, P., & Carvalho, L. (2014). Framing the analysis of learning network architectures. In L. Carvalho & P. Goodyear (Eds.), The architecture of productive learning networks. New York: Routledge.

Goodyear, P., Banks, S., Hodgson, V., & McConnell, D. (Eds.). (2004). Advances in research on networked learning. Dordrecht: Kluwer Academic Publishers.

Hodder, I. (2012). Entangled: an archaeology of the relationships between humans and things. Chichester: Wiley-Blackwell.

Hutchins, E. (2010). Cognitive ecology. Topics in Cognitive Science, 2, 705-715.

Ingold, T. (2007). Materials against materiality. Archaeological dialogues, 14(01), 1-16.

Ingold, T. (2011). Being alive: essays on movement, knowledge and description. Abingdon: Routledge.

Ingold, T. (2013). Making: anthropology, archaeology, art and architecture. Abingdon: Routledge.

Kirsh, D. (2013). Embodied cognition and the magical future of interaction design. ACM Transactions on Computer-Human Interaction, 20(1), 1-30. doi: 10.1145/2442106.2442109

Lonchamp, J. (2012). An instrumental perspective on CSCL systems. International Journal of Computer-Supported Collaborative Learning, 7(2), 211-237. doi: 10.1007/s11412-012-9141-4

Malafouris, L. (2013). How things shape the mind: a theory of material engagement. Cambridge, MA: MIT Press.

Malafouris, L., & Renfrew, C. (Eds.). (2010). The cognitive life of things: recasting the boundaries of the mind. Cambridge: McDonald Institute for Archaeological ResearchUniversity of Cambridge.

Mason, R., & Kaye, A. (Eds.). (1989). Mindweave: communication, computers and distance education. Oxford: Pergamon.

McConnell, D. (1994). Implementing computer supported cooperative learning. London: Kogan Page.

Periton, H., & Reynolds, M. (2014). ‘Here Be Dragons’: Approaching Difficult Group Issues in Networked Learning. In V. Hodgson, M. de Laat, D. McConnell & T. Ryberg (Eds.), The Design, Experience and Practice of Networked Learning (pp. 109-129). Heidelberg: Springer.

Rabardel, P., & Bourmaud, G. (2003). From computer to instrument system: A developmental perspective. Interacting with Computers, 15(5), 665–691.

Ritella, G., & Hakkarainen, K. (2012). Instrumental genesis in technology-mediated learning: From double stimulation to expansive knowledge practices. International Journal of Computer-Supported Collaborative Learning, 7(2), 239-258. doi: 10.1007/s11412-012-9144-1

Salmon, G. (2000). E-moderating: the key to teaching and learning online. London: Kogan Page.

Steeples, C., & Jones, C. (Eds.). (2002). Networked learning: perspectives and issues. London: Springer.

Sterelny, K. (2012). The evolved apprentice: how evolution made humans unique. Cambridge MA: MIT Press.

Wright, S., & Parchoma, G. (2014). Mobile learning and immutable mobiles: using iPhones to support informal learning in craft brewing. In V. Hodgson, M. de Laat, D. McConnell & T. Ryberg (Eds.), The Design, Experience and Practice of Networked Learning (pp. 241-261). Heidelberg: Springer.

Peter Goodyear

University of Sydney

 

 

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

petergoodyear3 Comments

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

New article on taking over someone else’s design

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Karen Scott (CoCo PhD 2012) has an article in the latest issue of Research in Learning Technology. Details below.

Taking over someone else’s e-learning design: challenges trigger change in e-learning beliefs and practices

Karen M. Scott

Abstract

As universities invest in the development of e-learning resources, e-learning sustainability has come under consideration. This has largely focused on the challenges and facilitators of organisational and technological sustainability and scalability, and professional development. Little research has examined the experience of a teacher dealing with e-learning sustainability when taking over a course with an e-learning resource and associated assessment. This research focuses on a teacher who was inexperienced with e-learning technology, yet took over a blended unit of study with an e-learning resource that accounted for one-fifth of the subject assessment and was directed towards academic skills development relevant to the degree program. Taking a longitudinal approach, this research examines the challenges faced by the new teacher and the way she changed the e-learning resource and its implementation over two years. A focus of the research is the way the teacher’s reflections on the challenges and changes provided an opportunity and stimulus for change in her e-learning beliefs and practices. This research has implications for the way universities support teachers taking over another teacher’s e-learning resource, the need for explicit documentation of underpinning beliefs and structured handover, the benefit of teamwork in developing e-learning resources, and provision of on-going support. Keywords: e-learning sustainability; e-learning beliefs and practices; reflection; longitudinal research (Published: 30 July 2014) Citation: Research in Learning Technology 2014, 22: 23362 – http://dx.doi.org/10.3402/rlt.v22.23362

Discovering Processes and Patterns of Learning in Collaborative Learning Environments Using Multi-modal Discourse Analysis

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Great work by Kate Thompson from the ARC Laureate team. This paper is available on open access from the journal website. It’s published in Research and Practice in Technology Enhanced Learning (RPTEL), the official journal of the Asia-Pacific Society for Computers in Education (APSCE).

Full reference: Kate Thompson, Shannon Kennedy-Clark, Lina Markauskaite and Vilaythong Southavilay (2014) Discovering Processes and Patterns of Learning in Collaborative Learning Environments Using Multi-modal Discourse Analysis, Research and Practice in Technology Enhanced Learning 9 (2), 215-40

Abstract:

Multimodal learning analytics, with a focus on discourse analysis, can be used to discover, and subsequently understand, the processes and patterns of learning in complex learning environments. Our work builds upon and integrates two types of research: (a) process analytic approaches of dynamically captured video and computer-screen activity and (b) learning analytics. By combining previous analyses of a dataset with new analyses of the processes of learning, patterns of successful and unsuccessful collaboration were identified. In this paper, the results of the application of a heuristics miner to utterances coded with the Decision-Function Coding Scheme, are combined with the results of First Order Markov transitions and in-depth linguistic analysis of the discourse to analyse the processes of collaborative problem solving within a scenario-based virtual world. The analysis of dependency graphs extracted from students’ event logs revealed problem solving actions enacted by students, as well as the dependency relationships between these actions. The addition of in-depth linguistic analysis explained the micro-level discourse of students, producing the observable patterns. Integration of these findings with those previously reported added to the depth of our understanding about this complex learning environment. We conclude with a discussion about the design of the tasks, the processes of collaboration, and the analytic approach that is presented in this paper.

Untold story: Design as Scholarship in the Learning Sciences

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Vanessa Svihla & Richard Reeve are putting together a book on this topic and are soliciting contributions. Some details below. Email vsvihla at unm.edu & reever at queensu.ca soon for further info. Closing date for chapter proposals is 30 July 2014.

UPDATE 2016: the book is now published; highly recommended – Svihla, V., & Reeve, R. (2016). Design as scholarship: case studies from the learning sciences. New York: Routledge.

Details (original call for chapters)

Learning scientists commonly report the design of an activity, object, or environment intended to produce some sort of learning or experience. The venues in which we publish typically do not encourage us to detail our designing as it occurred; this results in final form presentation of our work, which in turn leads to a picture of designing as deceptively straightforward. Worse, we argue, it provides little guidance about the reality of how researchers go about designing for learning. Those in the field are left to imagine how the process might have occurred and in turn are led to believe designing is simplistically phasic. In addition, the siren call for design principles is symptomatic of the felt-need for more certainty in our designs. Even in our tradition of conducting design-based research, designing is given short shrift, with much focus put to the designed product and how it instantiates a theory of learning. Design principles are treated both as a means to instantiate theory into design solutions and as an output of our work as a way to generalize findings. The former appears to stand in for client needs when design process is not reported; the latter can be difficult to use outside of the original context and can misfire or malfunction when applied piecemeal or superficially, without sufficient concern for how these principles may function in relationship to the local instructional context. Designing, particularly when client needs are also sought, can take on a distinctly emergent, even opportunistic form, and is typically iterative and even agile. Treating designing as unproblematic limits transferability of our work, and holds us back. Being honest about our designing has the potential to aid us — and others — in surfacing great ideas for learning. By, in effect, holding back on what we suggest are authentic aspects of designing, we may be limiting the new and improved ideas that could benefit the future of education.

The architecture of productive learning networks

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Cover - The Architecture of Productive Learning Networks[1]

Really pleased with the latest book from our ARC Laureate project.

It’s been a pleasure working with Lucila Carvalho (post-doc on the project and lead editor of the book). Lucila has done an amazing job in picking case study networks, assembling the team of authors, helping everyone tune in to the analytic framework and managing the million other tasks needed in getting a book from initial concept to final publication.

APLN has been a really useful way of developing skills, shared understanding and research profile within the Laureate team too: all the postdocs and PhD students have played a role in co-authoring chapters.

On Amazon here.

Networked Learning 2014

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NL2014 banner

We’re really looking forward to presenting at the 9th International Networked Learning Conference in Edinburgh next month. Four papers from the Laureate team:

Carvalho, L. & Goodyear, P. (2014). Analysing the structuring of knowledge in learning networks.

Goodyear, P., Carvalho, L. & Dohn, N. (2014). Design for networked learning: framing relations between participants’ activities and the physical setting.

Pinto, A. (2014). Design and functioning of a productive learning network.

Yeoman, P & Carvalho, L. (2014). Material entanglement in a primary school learning network.

and also one by our recent Visiting Scholar, Nina Bonderup Dohn:

Dohn, N (2014) A practice-grounded approach to ‘engagement’ and ‘motivation’ in networked learning

all to be found in Proceedings of the 9th International Conference on Networked Learning 2014, Edited by: Bayne S, Jones C, de Laat M, Ryberg T & Sinclair C.