Last week I had the pleasure of being welcomed into the Team-Based Learning community, at their Asia Pacific Community Symposium. While everyone is doing “team-based learning” of some sort, I was not familiar with TBL as a set of specific learning design patterns which have been refined across multiple contexts, with a dedicated and passionate community advocating and researching it, and dedicated platforms scaffolding the process.
They invited me to share how UTS has responded to GenAI, to which (given the community) I added a few pointers to work from my other work on Collective Intelligence and Teamwork Analytics. Slides below and as PDF.
As a Fellow of the RSA I’m happy to draw attention to the important new series of webinars just launched, on the critical role that effective, participatory deliberation has to play in resolving complex challenges, even apparently intractable dilemmas — at many different scales, from an organisation, to a local community, city, regional or even international scale.
As happens sometimes, I ended discovering a colleague at my own university doing fantastic work! Check out Nivek Thompson and her Deliberately Engaging portal. In prepping some notes for her, I thought I might as well blog them in case of wider interest to this community.
Hypermedia Discourse
A lot of my work has investigated a particular way in which software can help make thinking visible, the focus of all my work. Such tools seek to “augment human intellect” in Doug Engelbart‘s memorable words (my tribute to his inspiration for my work, and what he thought about this [Visualizing Argumentation]).
Here are some examples of how this works:
Make aspects of the conversational structure visible. Once a phenomenon is visible, rendered in a visual language that provides helpful ways to reflect on what is unfolding, it can be talked about, and is an “improvable object”. The Hypermedia Discourse project prototyped and evaluated the potential of combining models of dialogue and argumentation, with hypertext functionality for connecting issues, ideas, arguments and documents. Since we were interested in discourse about wicked problems, differences in perspective were the default starting point.
The Delta Dialogues is an outstanding example of our Compendium tool in action (view Meetings), in the hands of skilled facilitators, my longstanding colleagues Jeff Conklin and Eugene Eric Kim.
There are significant overlaps between educational technology focused on argumentation, and collective intelligence platforms for deliberation.
Support online forum moderators/facilitators assess the health of the conversation. A well designed user interface helps online participants to structure their contributions in ways that can provide the software with new ways to check the state of the debate (not possible with conventional flat chats, or threaded forums), and reflect this back to participants and/or moderators (see the Catalyst project for example).
Help to track ideas. Hypertext systems (more powerful than the Web) provide flexible ways to keep track of ideas (nodes), not just information. Anna De Liddo’s doctoral research is an example of how this can provide new forms of accountability in the participatory process ((in her work, for participatory urban design).
An important strand of our work was examining the facilitator skillset and disposition required to make good use of visualizations in real time, to augment the deliberation. Spearheaded by Al Selvin’s doctoral research, this led to a book that set out the concept of Knowledge Art.
Collaborative Evidence-based Problem-Solving
More recent work led by Tim van Gelder at Melbourne University (an Argument Mapping philosopher and software entrepreneur) has broken new ground in a particular niche of the design space: how do you convene a team of citizens to tackle a complex problem, with the challenge of devising an evidence-based, plausible analysis of the best way forward?
They have just published exciting results demonstrating that some teams of volunteers recruited via Facebook performed as well as, and in some cases better than, teams of professional intelligence analysts. See the paper to appear in the Journal of Cognitive Engineering and Decision Making on the Hunt Lab website, and this CIC webinar.
The emergence of NLP to detect critical, reflective writing
Natural Language Processing (NLP) has in recent years emerged from the AI labs into the mainstream. This has been a recent focus of my work, in the context of giving students instant feedback on their drafts. This has yet to be deployed in the context of participatory deliberation, but here are some preliminary reflections on where the automated detection of shallow and deeper reflection might assist participants posting online to reflect on how they are reacting to challenges — from other people, or the turbulent life events that are threatening dearly held assumptions, and ways of life.
Might the growing potential of NLP to make sense of rich, narrative prose offer the optimal combination in years to come — playing to the respective strengths of machines and humans to make sense of the world?
Power tools (and new literacy?) for deliberation professionals?
I remain excited about the potential of interactive, usable visualizations to help tackle the limitations of individual and collective human cognition. I have also seen first hand how hard it is for people to learn to structure their thinking more carefully than firing off their thoughts in the usual way. The role of the deliberation facilitator can be absolutely critical to modelling and scaffolding stakeholders into more reflective modes of reflective dialogue and rigorous argumentation.
That provides the basis for a good conversation with the growing international networks of deliberation experts who will also be working increasingly in online or hybrid modes.
This project is a collaboration between UTS:CIC and the University of Melbourne SWARM Project. The successful candidate will be based in Sydney, also spending time in Melbourne.
Visit the CIC PhD Scholarships page for full details. Please email us to express interest, ask any questions, and if we can see a potential fit we’ll advise you on writing your proposal.
The Challenge
The real world challenge: improving collaborative EBR
The challenges facing society are so complex that multiple expertises are needed. Consider security, science, law, health, policy-making, finance. The teamis the ubiquitous organisational unit, but the quality of its reasoning, especially under pressure, can vary dramatically. Problems are not provided in neat, well-defined packages: a team must frame problems in creative ways that lead to insights, and resolve uncertainties around possible responses, making the best possible use of evidence, plus their own judgement. Studies of how teams engage in such “sensemaking” highlight the blinkers that can blindside teams, and how the ways that the problem is expressed and visually represented can help or hinder (Weick, 1995). We will term this whole process Evidence-Based Reasoning (EBR). (We note of course that politics and social dynamics are unavoidable whenever people come together, and effective team members learn how to navigate these dynamics effectively.)
Improving collaborative EBR is an interesting scientific and design challenge. A successful support system (i.e. ways of working + enabling tools) must respect the principles of good reasoning, as determined by fields such as logic, argumentation and epistemology, and the domain-specific knowledge (i.e. emergency response, engineering, social work, counter-intelligence, etc.). At the same time, it must accommodate the strengths, weaknesses and vagaries of human reasoners, which is the terrain of cognitive and social psychologists. If part of the support system is interactive software, then it must have a good user interface and a solid underlying architecture. Assessing the resulting performance is a difficult evaluation problem. Building such systems is therefore inherently multidisciplinary.
How do we better equip teams for collaborative EBR? From an educational perspective, teamwork, problem solving and critical thinking skills are now among the most in demand ‘transferable competencies’ (Fiore et al 2018). The challenge of assessing and equipping graduates in these is at the heart of the learning and teaching strategies at UTS and U. Melbourne.
The technology support challenge:
While in some fields, there are specialist tools for modelling and simulation that assist analysts by managing constraints in the problem, but even with machine intelligence, the agency typically rests with the human analysts to decide how much weight to give to the machine’s output. Most other fields, however, do not have such tools: collaborative EBR is typically supported by general-purpose information technologies such as word processors, spreadsheets, databases, and project planners to help with managing information and producing reports. Similarly, generic communication tools dominate, such as email, chat, video conferencing, phone. In most cases, the reasoning itself is typically left wholly to the human reasoners themselves.
There have been remarkably few attempts to provide direct technological support for the processes of inference and judgement that are at the heart of collaborative EBR, and moreover, those attempts have had little impact on the way it is actually conducted in most places (van Gelder, 2012). There are methods and software tools for facilitating group processes and visualising team reasoning, but these require quite an advanced facilitation and software skillset (e.g. Culmsee and Awati, 2013; Okada, et al., 2008; Selvin et al, 2012).
Our interest is in developing computer-support to improve the collaborative EBR of geographically and often temporally distributed teams, that does not require specialist skills to start using beyond using what are now familiar collaboration tools. SWARM is an online platform emerging from an ongoing research project to improve the kind of collaborative EBR undertaken by intelligence analysts making sense of complex sets of qualitative and quantitative information or varying reliability. However, these are the conditions under which most other domains operate, and we hypothesise that it has broader potential, and specifically in this project, for education and training. SWARM is based on three design principles: cultivating user engagement, exploiting natural expertise, and supporting rich collaboration (van Gelder et al, 2018). Central to its approach is the upskilling of team members to equip them with different EBR skills (see in particular the Lens Kit).
Figure: The SWARM workspace
Recent large scale empirical evaluations, in which teams of analysts tackled complex challenges with or without SWARM, indicated that the quality of the reports produced by SWARM teams was significantly better than reports produced by analysts using normal methods (van Gelder et al, In Prep). In a follow-up project, “super-teams” on the platform produced reasoning so good it would plausibly be called “super-reasoning” (van Gelder & de Rozario, 2017) analogous to “super-forecasting” (Tetlock & Gardner, 2015).
This CIC seminar is a great introduction to the work so far:
Learning Analytics for SWARM
The encouraging evidence of SWARM’s effectiveness makes it an attractive candidate platform for use in educational/training contexts. While evaluation of final reports (i.e. the team’s product) is a conventional measure of team performance, and certainly one that educators will be interested in, this is not the only possible indicator of improvement. The emergence of data science, activity-based analytics and visualisation opens new possibilities for tracking the process that teams are following. Learning Analytics connects such techniques to what is known about the teaching and learning of teamwork, and could make the assessment of team performance more rigorous, and more cost effective.
This PhD is therefore focusing on inventing and validating new forms of automated team analytics for collaborative EBR, to provide insights into both process and product. Such analytics might enable not only coaches and researchers to gain insights into a team’s effectiveness, but the teams themselves to monitor their work in real time, or critically review their project on completion. Further, real-time analytics can be used to shape the collaborative environment itself, resulting in better collaboration and better outputs. Some prototype analytics have already been developed to summarise participants’ contributions and interactions. This PhD will build on this work, synthesise the literature, plus insights from the SWARM team and educators, in order to define, design, implement and evaluate automated analytics in different contexts, spanning education and training, research, and potentially more authentic deployments with professional teams.
Figure: Early version of the SWARM group dynamics dashboard. Upper diagrams shows levels of interaction among team members working on a particular problem.
Relevant analytics techniques include, but are not limited to:
Text analysis to identify significant contributions to the team communications and the report they are producing
Social network analysis to identify significant interaction patterns among team members
Process mining to identify significant sequences in the actions that individuals engage in, within or between sessions
Statistical techniques to identify significant differences between teams
Candidates
In addition to the broad skills and dispositions that we are seeking in all candidates (see CIC’s PhD homepage), you should have:
A Masters degree, Honours distinction or equivalent with at least above-average grades in computer science, mathematics, statistics, or equivalent
Analytical, creative and innovative approach to solving problems
Strong interest in designing and conducting quantitative, qualitative or mixed-method studies
Strong programming skills in at least one relevant language (e.g. R, Python)
Experience with web log analysis, statistics and/or data science tools.
It is advantageous if you can evidence:
Design and Implementation of user-centred software, especially data/information visualisations
Skill in working with non-technical clients to involve them in the design and testing of software tools
Knowledge and experience of natural language processing/text analytics
Familiarity with the scholarship in a relevant areas (e.g. high performance teams; collective intelligence; collaborative problem solving)
We will discuss your ideas with you to help sharpen up your proposal, which will be competing with others for a scholarship. Please follow the application procedure for the submission of your proposal.
Fiore, S. M., Graesser, A., & Greiff, S. (2018). Collaborative problem-solving education for the twenty-first-century workforce. Nature Human Behaviour, 2(6), 367–369.
van Gelder, T., & de Rozario, R. (2017). Pursuing Fundamental Advances in Human Reasoning. In T. Everitt, B. Goertzel, & A. Potapov (Eds.), Artificial General Intelligence(Vol. 10414, pp. 259–262). Cham: Springer International Publishing.
I’m just back from an amazing workshop at Bristol. This is a step along the path of an unfolding story about how we are using the groundbreaking work of Tony Bryk‘s team on Design, Educational Engineering and Development (DEED), as a methodology for systemic school change. This is combined with the University of Bristol’s Effective Lifelong Learning Inventory (ELLI), which forms part of a process of Authentic Inquiry (AI) for students, teachers and leaders.
My part of this systemic approach is that the insights from these prototypes are then shared via our Collective Intelligence Evidence Hub. My introduction to this is below, but see the full story on Learning Emergence to understand how this fits into the whole approach, and the demos I then give after teachers’ presentations.
Oasis Academies intend to use the Hub as a key way to distill and share staff learning across the region’s academies. This is an exciting convergence, with the academy piloting this reports that it has catalysed a profound shift in how they think about professional development.
Put simply, Doug Engelbart is the inspiration for my professional career.
Doug spent a lifetime explaining to people how things might be: that human-computer systems could augment human intellect. Firstly, in an era when everyone else was feeding punch-cards to electronic wardrobes, he showed how and why computers could be live extensions to our individual minds. In that extraordinary 1968 Mother of All Demos (Archive / Highlights / Wikipedia), showed a computing conference audience some very weird things his SRI team had been developing: a mouse, editing text directly on the screen, hyperlinks, collaborative editing, video-conferencing. Those breakthroughs went on to further development at Xerox PARC, and then into products via Apple, and finally on to Windows, as is well documented.
This in itself takes us way past the man who invented the mousenews headlines of this week.
But his real vision was on how massive connectivity could improve our collective intelligence, and ability to tackle the “complex, urgent problems” facing humanity. If augmenting an individual’s intellect is hard, augmenting many minds to work together is orders of magnitude tougher.
The complex urgent challenges aren’t going away, but concepts of Collective IQ and Networked Improvement Communities are gaining traction technically and conceptually.
Living in a future few others see is not an easy calling. Thank you for your resilience, and for what you have given humanity. May we make it an increasingly embedded reality.
The European FET Flagships competition has the following mission:
FET Flagships are ambitious large-scale, science-driven, research initiatives that aim to achieve a visionary goal. The scientific advance should provide a strong and broad basis for future technological innovation and economic exploitation in a variety of areas, as well as novel benefits for society.
As part of the FuturICT submission from a year-long pilot, we recently submitted our proposal for the first 30months of a 10 year research programme (PDF).
FuturlCT is a FET Flagship project using collective, participatory research, integrated across the fields of ICT, the social sciences and complexity science, to design socio-‐inspired technology and develop a science of global, socially interactive systems. The project will bring together, on a global level, Big Data, new modelling techniques and new forms of interaction, leading to a new understanding of society and its co-‐ evolution with technology. It will place Europe at the forefront of a major scientific drive to understand, explore and manage our complex, connected world in a more sustainable and resilient manner.
Working with FuturICT is the closest that someone in my field may get to “Big Science” a la Human Genome project or Large Hadron Collider. We don’t tend to have projects of that scale in human-centred computing! But the over-arching theme of Complexity Science as a way of making sense of societal big data provides that scale of vision. The European Physical Journal does not spring to mind as the first place to look for work on ICT for learning analytics or collective intelligence – my particular interests – but it has an explicit focus on advances in Complex Systems, including socio-technical-economic systems, not just physical or biological. So it’s very satisfying to point to a more detailed account of the thinking behind the proposal, which we’ve just published as an open access special issue of EPJST.
Within the special issue, you’ll find a fascinating set of contributions from European scientists who set out a 10 year research agenda within their fields: what are the really tough problems? There are also visionary position papers outlining the kind of socio-technical infrastructure that FuturICT will investigate, and a foregrounding of the ethical dimensions that human Big Data and Analytics always raise.
For those interested in Human-Centred Computing, CSCW, Collective Intelligence and Learning, these run implicitly through many of the articles, but I contributed on these themes to two of them:
The FuturICT project seeks to use the power of big data, analytic models grounded in complexity science, and the collective intelligence they yield for societal benefit. Accordingly, this paper argues that these new tools should not remain the preserve of restricted government, scientific or corporate élites, but be opened up for societal engagement and critique. To democratise such assets as a public good, requires a sustainable ecosystem enabling different kinds of stakeholder in society, including but not limited to, citizens and advocacy groups, school and university students, policy analysts, scientists, software developers, journalists and politicians. Our working name for envisioning a sociotechnical infrastructure capable of engaging such a wide constituency is the Global Participatory Platform (GPP). We consider what it means to develop a GPP at the different levels of data, models and deliberation, motivating a framework for different stakeholders to find their ecological niches at different levels within the system, serving the functions of (i) sensing the environment in order to pool data, (ii) mining the resulting data for patterns in order to model the past/present/future, and (iii) sharing and contesting possible interpretations of what those models might mean, and in a policy context, possible decisions. A research objective is also to apply the concepts and tools of complexity science and social science to the project’s own work. We therefore conceive the global participatory platform as a resilient, epistemic ecosystem, whose design will make it capable of self-organization and adaptation to a dynamic environment, and whose structure and contributions are themselves networks of stakeholders, challenges, issues, ideas and arguments whose structure and dynamics can be modelled and analysed.
Johnson, J., Buckingham Shum, S., Bishop, S., Zamenopoulos, T., Swithenby, S., MacKay, R., Merali, Y., Lorincz, A., Costea, C., Bourgine, P., Louçã, J., Kapenieks, A., Kelley, P., Caird, S., Bromley, J., Deakin Crick, R., Goldspink, C., Collet, P., Carbone, A. and Helbing, D. (2012). The FuturICT Education Accelerator.Eur. Phys. J. Special Topics, 214, pp.215-243. http://dx.doi.org/10.1140/epjst/e2012-01693-0
Education is a major force for economic and social wellbeing. Despite high aspirations, education at all levels can be expensive and ineffective. Three Grand Challenges are identified: (1) enable people to learn orders of magnitude more effectively, (2) enable people to learn at orders of magnitude less cost, and (3) demonstrate success by exemplary interdisciplinary education in complex systems science. A ten year ‘man-on-the-moon’ project is proposed in which FuturICT’s unique combination of Complexity, Social and Computing Sciences could provide an urgently needed transdisciplinary language for making sense of educational systems. In close dialogue with educational theory and practice, and grounded in the emerging data science and learning analytics paradigms, this will translate into practical tools (both analytical and computational) for researchers, practitioners and leaders; generative principles for resilient educational ecosystems; and innovation for radically scalable, yet personalised, learner engagement and assessment. The proposed Education Accelerator will serve as a ‘wind tunnel’ for testing these ideas in the context of real educational programmes, with an international virtual campus delivering complex systems education exploiting the new understanding of complex, social, computationally enhanced organisational structure developed within FuturICT.
In the above paper, my collaboration around LearningEmergence.net with Ruth Deakin Crick (Univ. Bristol) and Chris Goldspink (Incept Labs) opens up for us an intriguing landscape around complexity thinking and learning:
“As in many other fields, there is now active interest in the possibility that the concepts and tools of complexity science hold the promse of providing a new, more rigorous language and suites of computational tools for systemic thinking within educational research. These could enable possible futures to be mapped, modelled, simulated, and rendered in appropriate forms to help both researchers and practitioners to understand and, where appropriate, choose to act differently to achieve their intended outcomes. A central claim to be investigated in this research programme is that complexity science provides a language for transdisciplinary learning-centred discourse between system stakeholders, serving as reference points for modelling and, suitably communicated and embodied in tools, for educational leaders, and learners.
For instance, autopoiesis is relevant to the emergence of learner identity in co-constructed domains of meaning, and hence for the way we approach learning as well as school change. Dissonance, defined as conflict between agents and processes, creates a space for deep learning when agents have the capacity to hold conflicting ideas in tension. Emergence focuses attention on the quality of relationships for creative learning and leadership in complex organisations. Resilience has been identified as key to learning to learn, and has been operationalised as a formally modellable quality in individual learners, not just socio-technical collectives [9].
To take another example, the evidence is that efforts to manage educational systems (whether at national or institutional level) which do not take into account complex systems dynamics, do not result in sustained school improvement: standards in schools across the developed world are plateauing, as measured by student outcomes [23]. There is a pressing need for management and self evaluation processes [24] which can account for such complexity in order to facilitate, value and enhance the breadth and range of student outcomes. The evidence emerging from these new approaches is that systemic transformation is indeed possible (e.g. [25–28]).
A research community is now emerging at the intersection of Complexity Science, Educational Theory and Practice [24, 28, 30–33]. Through our visiting scholars programme, and international workshop and webinar series, we anticipate a very productive dialogue with these networks. FuturICT will make available unprecedented computational infrastructure for tracking and modelling complex systems — the question is how does this contribute to current theoretical discourse, and how can intensely practical challenges around the design and management of resilient learning ecosystems be tackled in fresh ways when traditional theory is combined with simulation and visualisation tools that can render complex systems in new ways, for both researchers and practitioners?”
Working with the FuturICT consortium has been a fascinating experience so far, and if we are successful, the start of what should be a remarkable intellectual journey. For great introductions to the field, we’re working with Philip Ball, an award-winning science journalist. He’s written a brilliant historical contextualisation of the notion of a “physics of society”, controversial as this is — check out Critical Mass – and is helping us explain Why Society is a Complex Matter.
As part of our Collective Intelligence R&D, for the last few months we’ve been developing the concept of an Evidence Hub, learning user experience lessons from the first example we developed for the Hewlett Foundation on the Open Learning Network project, generifying the shell into one that can be customized for the different communities who have been excited by the concept, and moving to an open source release shortly.
Here’s the latest intro movie, with more info and demos at Evidence-Hub.net
If you want to read more about the rationale, check out:
De Liddo, Anna; Buckingham Shum, Simon; McAndrew, Patrick and Farrow, Robert (2012). The open education evidence hub: a collective intelligence tool for evidence based policy. Presented at Cambridge 2012: Joint OER12 and OpenCourseWare Consortium Global 2012 Conference, 16 – 18 April 2012, Cambridge, UK. Eprint: http://oro.open.ac.uk/33253
FuturICT, one of the six Flagship pilot projects under consideration by the EC, has just completed the 1 year process of sharpening up its mission and consortium. Now, with other OU colleagues Jeff Johnson and John Domingue, we’re developing the full proposal.
I’m involved in defining the Global Participatory Platform, which is outlined in this presentation (from slide 20) to the FET Flagship panel and others:
From our very fruitful collaboration with Ágnes Sándor (Xerox Research Centre Europe, Grenoble), comes this joint journal paper, setting out the conception of Contested Collective Intelligence that we’ve been developing in KMi, as exemplified through Cohere (human social-semantic web annotation and knowledge cartography) plus machine text analysis using Xerox Incremental Parser (mining written text for patterns signifying knowledge level claims and argumentative moves). This article will appear as part of a forthcoming special issue of CSCW journal distilling work from several workshops on collective intelligence in organizations led by Gregorio Convertino:
We propose the concept of Contested Collective Intelligence (CCI) as a distinctive subset of the broader Collective Intelligence design space. CCI is relevant to the many organizational contexts in which it is important to work with contested knowledge, for instance, due to different intellectual traditions, competing organizational objectives, information overload or ambiguous environmental signals. The CCI challenge is to design sociotechnical infrastructures to augment such organizational capability. Since documents are often the starting points for contested discourse, and discourse markers provide a powerful cue to the presence of claims, contrasting ideas and argumentation, discourse and rhetoric provide an annotation focus in our approach to CCI. Research in sensemaking, computer-supported discourse and rhetorical text analysis motivate a conceptual framework for the combined human and machine annotation of texts with this specific focus. This conception is explored through two tools: a social-semantic web application for human annotation and knowledge mapping (Cohere), plus the discourse analysis component in a textual analysis software tool (Xerox Incremental Parser: XIP). As a step towards an integrated platform, we report a case study in which a document corpus underwent independent human and machine analysis, providing quantitative and qualitative insight into their respective contributions. A promising finding is that significant contributions were signalled by authors via explicit rhetorical moves, which both human analysts and XIP could readily identify. Since working with contested knowledge is at the heart of CCI, the evidence that automatic detection of contrasting ideas in texts is possible through rhetorical discourse analysis is progress towards the effective use of automatic discourse analysis in the CCI framework.
Like our other knowledge cartography software tool Compendium, Cohere is a ‘horizontal’ application: it provides an extremely customizable visual language, and is agnostic as to the user community or field of application. This is what you want from a research platform that can serve as a vehicle for experimenting with new ideas, but the tradeoff is that huge customizability and high functionality makes a tool more complex for users who want it to do a specific job.
As part of our work with the Institute of Educational Technology in the OLnet project, we have been exploring the creation of a ‘vertical’ app from Cohere, in this case, to pool collective intelligence for the Open Education movement: a living map by, for and about the community, and those it seeks to impact. This has required us to strip down the visual language to a core set of entities and relations which we hypothesise could serve the needs of this community, but also many other communities of enquiry: Key Challenges, Issues, Proposed Solutions, Research Claims, Evidence, Resources, Organizations, Projects and People. In addition, we define a set of core themes derived from the project’s analysis of the Open Education field, and new ways to quickly add semantically typed ideas and connections (without being confronted by a visualization of a semantic triple as in Cohere).
This work has resulted in what we call an Evidence Hub, and a new widget-matrix we call the Explore view, which navigates the underlying network one focal-node at a time, refreshing the nodes displayed around it in the other widgets.
This 7min movie gives a quick demo of the Hub in use:
This longer 15min movie demonstrates some more of its functionality, including network visualizations and how we add new contributions.
We are now generalising the concept to other communities, as described in these slides, in order to test the building blocks in new contexts. By the summer we’ll have an open source release:
ACM CSCW 2012, 11th February 2012 – Seattle, Washington
Collective Intelligence (CI) research investigates the design of infrastructures to enable collectives to think and act intelligently, and intriguingly, more intelligently than individuals. Technologies such as idea management or argumentation tools, blogs, wikis, chats, forums, Q&A sites, and social networks provide unprecedented opportunities for entire communities or organizations to express a discourse and act at a massive scale.
This workshop seeks to understand the forms of CI that can be constructed through discourse and action, which enables advanced forms of collective sensemaking such as idea generation and prioritization, argumentation, and deliberation.
When does effective discourse help a collective outperform individuals?
What functions should the next generation of social platforms support?
How can we allow communities to efficiently manage many diverse ideas, argue, and deliberate?
What patterns in discourse and action can be modeled computationally?
We welcome contributions on these and other relevant questions through Research Papers, Position Papers, and Demos.
Organisers
Anna De Liddo
Knowledge Media Institute, Open University UK
Simon Buckingham Shum
Knowledge Media Institute, Open University UK
Gregorio Convertino
Xerox Research Centre Europe & PARC
Ágnes Sándor
Xerox Research Centre Europe
Mark Klein
Center for Collective Intelligence, MIT
I recently gave an EDUCAUSE webinar, in which I told the story that’s slowly emerging from the last year’s work around Social Learning, Sensemaking Capacity, and Collective Intelligence. Thanks to the ELI team for inviting me to share this work, and we’re now working on an article to consolidate this. It was recently one of the tweeted presentations on Slideshare, which was encouraging!
Some of the videos of Hypermedia Discourse tools, which I didn’t get time to show, are blogged here. Slideshare below, PPT on EDUCAUSE website, or PDF.
