Tag: argumentation

“The Future of Text” hits the streets

Congratulations to Frode Heglund, convenor for 9 years of the widely respected Future of Text Symposium, who has just edited and published The Future of Text book! The book is “a letter to the distant future”, in which an astonishingly diverse group of people share perspectives on where humanity has got to with the symbol systems we use to codify and share ideas — “Text” — and where we might be going. I was honoured to be invited to contribute to this: The Future of Text in Three Moves.

From the book’s introduction:

“I have invited some truly phenomenal people to contribute to this work, and against all odds, they said yes. There are eminent representatives from the worlds of art, typography, science, software development, academia and more. Text writes our history, and text guides our future, but text itself is not often reflected on, let alone written about.”

You can replay videos from the launch event, which was introduced by no less than Vint Cerf who has supported this work from its early days:

Well it’s been a hell of a year, so here’s a book to settle down with over Christmas and New Year, accompanied by your favourite brew, to dip into at random and make some space for reflection. Give it to all your friends and family! The pieces are short (some only a page), and a mind-stretching joy to browse.

I’m old enough to still love the weighty-tome gravitas of the printed volume in the above photo, but of course, most of us will read this digitally. It’s freely available as PDF | EPUB, and the PDF takes on greater interactive qualities when viewed in Frode’s Reader hypertext tool. Here’s his demo of this and the other tools he’s been inventing…

Alert to the challenge of digital preservation (“we’re writing in sand” as Frode memorably states about digital texts) — they’re working on a physical edition with the entire text micro-engraved into plastic and metal for longevity.

Frode kindly invited me to contribute a chapter, which gave me a much appreciated pause for thought on the 30 years I’ve spent using hypertext to create interactive visualisations of dialogue and arguments, and in the last decade or so, learning from my NLP and machine learning colleagues. So, here’s my letter to the future: The Future of Text in Three Moves.

Many congratulations also to Niko Grupen, whose piece From Author to Editor: Our Place Alongside the New Life Cycle of Text (p.148) scooped first prize in the Student Competition! His punishment is to read a signed Visualizing Argumentation e-book cover-to-cover over Christmas 😉

Doug Engelbart (RIP) is the fire driving both Frode and me (his influence on my career) — so for old time’s sake, here’s when Doug and Frode Heglund visited me in 2004 🙂

2 tools to help PhD students make their thinking visible

In this post, I share two ways to map your thinking, at different scales (rather like geographical maps)…

CQOCE Diagrams (or “Thesis Maps”)

One of the challenges that most/all PhD students have is to make their thinking visible — to themselves, to their supervisors, and to other researchers. There are so many potential ideas to weave into a narrative, and often different narrative pathways.

In our Learning Analytics PhD Program, we’ve been using the unpronounceable but very useful CQOCE diagram described by Luis Prieto in his Happy PhD blog. I encourage you to learn more about this:

“the diagram is commonly used in the introduction section of a dissertation, and it is meant to introduce, in graphical form, some of its main elements: the research Context, main research Question, Objectives, Contributions of your thesis and their Evaluation. However, many of us have also used it way before starting to write the dissertation book itself, as a “guiding star” when discussing with others and planning the thesis work.”

We have also been using it not just as a writing up device, but as a challenge right in the first year, to get doctoral researchers thinking about their thesis story. I tend to just call it the Thesis Map! As Luis comments, this goes through many revisions as the PhD takes its twists and turns. So in the end it serves two key purposes:

  1. As a mirror for the supervision team to reflect on how we’re doing — “Oh, the contributions I thought I was making don’t align with the Research Questions…” “What kind of evaluation will be needed next year to back up this claimed Contribution 2?…”
  2. As a navigational aid — a map — for the reader of the thesis, or indeed, for the audience if it’s used in a talk (“…in this talk I’ll be covering only this part of the map, but it shows you how this is a stage in a longer journey, in a  wider landscape…”)

Here are two recent Learning Analytics theses that use this, from Vanessa Echeverria and Carlos Prieto.

Echeverria, V. (2020). Designing Feedback for Collocated Teams using Multimodal Learning Analytics

Prieto-Alvarez, C.G. (2020), Engaging Stakeholders in the Learning Analytics Design Process

        

Note that sections of the map can then be introduced in each chapter, to remind the reader where we are on the journey.

Argument Maps

The Thesis Map provides a macro-structure for the thesis argument: once you’ve bashed your map into shape, then your high level claim to have evidenced contributions to advance knowledge that addresses important RQs just drops out naturally. But there will be many micro-level arguments in the thesis that are invisible at this scale.

Zooming in, we’re experimenting with Argument Maps, that make visible more detailed moves. Here’s my briefing during a PhD session which introduces some basics…

2 weeks later, a couple of researchers shared their maps for feedback, and both commented on how it helps clarify thinking. Thanks to Ben Hicks and Gloria Fernandez-Nieto for jumping in!

Ben used the freely accessible ArgDown website which uses a classic Argument Map notation, enriched with optional colour-coding from #tags:

Gloria used the free Compendium tool that my team developed at KMi Open University, using the IBIS notation (QuickStart Guide to install):

Note: Al Selvin inventor and power user of Compendium, used it to create multimedia maps of his thesis thinking and qualitative data analysis [screen demos], incorporating many kinds of documents (which can be dragged and dropped onto maps).

    

Far more info on Knowledge Cartography is available if this interests you.

I hope these help you make your own thinking more visible — to yourself, your supervisors, and the world  🙂

OpenU at LAK13

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OpenU contributions in Leuven at LAK13 next month…

Update: all presentations now online

Buckingham Shum, S., Baker, R., Behrens, J., Hawksey, M., Jeffery, N. and Pea, R. (2013). Educational Data Scientists: A Scarce Breed (Panel). Proc. LAK13: 3rd International Conference on Learning Analytics & Knowledge, 8-12 April 2013, Leuven, Belgium (ACM: New York). Open Access Eprint: https://simon.buckinghamshum.net/2013/03/lak13-edu-data-scientists-scarce-breed

The Educational Data Scientist is currently a poorly understood, rarely sighted breed. Reports vary: some are known to be largely nocturnal, solitary creatures, while others have been reported to display highly social behaviour in broad daylight. What are their primary habits? How do they see the world? What ecological niches do they occupy now, and will predicted seismic shifts transform the landscape in their favour? What survival skills do they need when running into other breeds? Will their numbers grow, and how might they evolve? In this panel, the conference will hear and debate not only broad perspectives on the terrain, but will have been exposed to some real life specimens, and caught glimpses of the future ecosystem. [Post your questions to the Panellists…]

Buckingham Shum, S., De Laat, M., De Liddo, A., Ferguson, R., Kirschner, P., Ravencroft, A., Sándor, Á. and Whitelock, D. (2013). 1st International Workshop on Discourse-Centric Learning Analytics. LAK13: 3rd International Conference on Learning Analytics & Knowledge, 8 April 2013, Leuven, Belgium. http://www.solaresearch.org/events/lak/lak13/dcla13

Written discourse is a major class of data that learners produce in online environments, arguably the primary class of data that can give us insights into deeper learning and higher order qualities such as critical thinking, argumentation, mastery of complex ideas, empathy, collaboration and interpersonal skills. It is central to the collaborative and social learning that takes place online and there is a correspondingly significant literature on discourse analysis for online learning/CSCL. Computational linguistics research has developed a rich array of automated tools for machine interpretation of human discourse, but work to develop these tools in the context of learning is at a relatively early stage. Moreover, there is a significant difference between the use of such tools to assist researchers in discourse analysis, and their deployment on platforms in order to provide meaningful analytics for learners and educators. A major class of learning analytic will emerge at the intersection of research into learning dynamics, deliberation platforms, and computational linguistics. What will make these learning analytics, as opposed to research that sits in any of the above categories, will be their use to generate information displays that help learners and/or educators to understand where significant discourse patterns are happening and that support interventions to improve discourse for learning.

Clow, D. (2013). MOOCs and the Funnel of Participation. Proc. LAK13: 3rd International Conference on Learning Analytics & Knowledge, 8-12 April 2013, Leuven, Belgium (ACM: New York). Open Access Eprint: http://oro.open.ac.uk/36657

Massive Online Open Courses (MOOCs) are growing substantially in numbers, and also in interest from the educational community. MOOCs offer particular challenges for what is becoming accepted as mainstream practice in learning analytics.

Partly for this reason, and partly because of the relative newness of MOOCs as a widespread phenomenon, there is not yet a substantial body of literature on the learning analytics of MOOCs. However, one clear finding is that drop-out/non-completion rates are substantially higher than in more traditional education.

This paper explores these issues, and introduces the metaphor of a ‘funnel of participation’ to reconceptualise the steep drop-off in activity, and the pattern of steeply unequal participation, which appear to be characteristic of MOOCs and similar learning environments. Empirical data to support this funnel of participation are presented from three online learning sites: iSpot (observations of nature), Cloudworks (‘a place to share, find and discuss learning and teaching ideas and experiences’), and openED 2.0, a MOOC on business and management that ran between 2010-2012. Implications of the funnel for MOOCs, formal education, and learning analytics practice are discussed.

d’Aquin, M. and Jay, N. (2013). Interpreting Data Mining Results with Linked Data for Learning Analytics: Motivation, Case Study and Direction. Proc. LAK13: 3rd International Conference on Learning Analytics & Knowledge, 8-12 April 2013, Leuven, Belgium (ACM: New York). Open Access Eprint: http://oro.open.ac.uk/36660

Learning analytics by nature relies on computational information processing activities intended to extract from raw data some interesting aspects that can be used to obtain insights into the behaviours of learners, the design of learning experiences, etc. There is a large variety of computational techniques that can be employed, all with interesting properties, but it is the interpretation of their results that really forms the core of the analytics process. In this paper, we look at a specific data mining method, namely sequential pattern extraction, and we demonstrate an approach that exploits available linked open data for this interpretation task. Indeed, we show through a case study relying on data about students’ enrolment in course modules how linked data can be used to provide a variety of additional dimensions through which the results of the data mining method can be explored, providing, at interpretation time, new input into the analytics process.

d’Aquin, M., Dietze, S., Drachsler, H. and Herder, E. (2013). Tutorial: Using Linked Data in Learning Analytics. LAK13: 3rd International Conference on Learning Analytics & Knowledge, 8-12 April 2013, Leuven, Belgium. http://linkedup-project.eu/2013/03/17/using-linked-data-in-learning-analytics-a-tutorial-by-the-linkedup-consortium

Linked Data is a set of principles and technologies aimed at using the architecture of the web to share, expose and integrate data in a global, collaborative space. This tutorial intends to provide Learning Analytics practitioners with the basic knowledge and skills required to exploit the new possibilities offered by linked data, especially through exploring the wealth of data sources already available in the linked data cloud. We will therefore introduce the basic technologies and practices generally associated with Linked Data, including graph-­based data modelling with RDF and relevant vocabularies, data discovery on the linked data cloud and the use of linked data endpoints (with SPARQL). Since the focus of the tutorial is on the concrete use of these technologies and practices within a Learning Analytics scenario, a large part of the sessions will be dedicated to hands-­on exercises with data and use cases of relevance to Learning Analytics.

In addition, the tutorial will be used as a channel to present initial outcomes of the LinkedUp project, like the LinkedUp data pool and the LinkedUp Evaluation Framework. Participants to the tutorial will be encouraged to push further their ideas regarding the possible applications of Linked Data in Learning analytics scenarios through collaborating with members of LinkedUp and participating to the LinkedUp Challenge: the application development competition organized by the project. These particular activities will be concretely materialized through the inclusion as key sessions in the tutorial of activities around the LinkedUp­‐supported “LAK Data Challenge”, as well as interactive brainstorming sessions around possible use cases for linked data in Learning Analytics scenarios, and their possible realisation.

d’Aquin, M., Dietze, S., Drachsler, H., Herder, E. and Taibi, D. (2013). The LAK Data Challenge. LAK13: 3rd International Conference on Learning Analytics & Knowledge, 8-12 April 2013, Leuven, Belgium. http://www.solaresearch.org/events/lak/lak-data-challenge

What do analytics on learning analytics tell us? How can we make sense of this emerging field’s historical roots, current state, and future trends, based on how its members report and debate their research? The LAK Dataset provides access to structured metadata from research publications in the field of learning analytics. Challenge submissions should exploit the LAK Dataset for a meaningful purpose. This may include submissions which cover one or more of the following, non-exclusive list of topics:

  • Analysis & assessment of the emerging LAK community in terms of topics, people, citations or connections with other fields
  • Innovative applications to explore, navigate and visualise the dataset (and/or its correlation with other datasets)
  • Usage of the dataset as part of recommender systems

Ferguson, R., Wei, Z., He, Y. and Buckingham Shum, S. (2013). An Evaluation of Learning Analytics to Identify Exploratory Dialogue in Online Discussions. Proc. LAK13: 3rd International Conference on Learning Analytics & Knowledge, 8-12 April 2013, Leuven, Belgium (ACM: New York). Open Access Eprint: http://oro.open.ac.uk/36664

Social learning analytics are concerned with the process of knowledge construction as learners build knowledge together in their social and cultural environments. One of the most important tools employed during this process is language. In this paper we take exploratory dialogue, a joint form of co-reasoning, to be an external indicator that learning is taking place. Using techniques developed within the field of computational linguistics, we build on previous work using cue phrases to identify exploratory dialogue within online discussion. Automatic detection of this type of dialogue is framed as a binary classification task that labels each contribution to an online discussion as exploratory or non-exploratory. We describe the development of a self-training framework that employs discourse features and topical features for classification by integrating both cue-phrase matching and k-nearest neighbour classification. Experiments with a corpus constructed from the archive of a two-day online conference show that our proposed framework outperforms other approaches. A classifier developed using the self-training framework is able to make useful distinctions between the learning dialogue taking place at different times within an online conference as well as between the contributions of individual participants.

Knight, S., Buckingham Shum, S. and Littleton, K. (2013). Epistemology, Pedagogy, Assessment and Learning Analytics. In: Proc. LAK13: 3rd International Conference on Learning Analytics & Knowledge, 8-12 April 2013, Leuven, Belgium (ACM: New York). Open Access Eprint: http://oro.open.ac.uk/36635

There is a well-established literature examining the relationships between epistemology (the nature of knowledge), pedagogy (the nature of learning and teaching), and assessment. Learning Analytics (LA) is a new assessment technology and should engage with this literature since it has implications for when and why different LA tools might be deployed. This paper discusses these issues, relating them to an example construct, epistemic beliefs – beliefs about the nature of knowledge – for which analytics grounded in pragmatic, sociocultural theory might be well placed to explore. This example is particularly interesting given the role of epistemic beliefs in the everyday knowledge judgements students make in their information processing. Traditional psychological approaches to measuring epistemic beliefs have parallels with high stakes testing regimes; this paper outlines an alternative LA for epistemic beliefs which might be readily applied to other areas of interest. Such sociocultural approaches afford opportunity for engaging LA directly in high quality pedagogy.

Knight, S. and Littleton, K. (2013). Discourse, Computation and Context – Sociocultural DCLA Revisited. 1st International Workshop on Discourse-Centric Learning Analytics. LAK13: 3rd International Conference on Learning Analytics & Knowledge, 8 April 2013, Leuven, Belgium. http://oro.open.ac.uk/36640

This paper expands the sociocultural analysis of earlier discourse centric learning analytics (DCLA) to discuss the pedagogic functions of discourse, and the implications of these functions for DCLA. Given the importance of discourse for learning [13], and the potential of computers to (a) scaffold effective discourse and (b) give meaningful feedback on such discourse, it is important that DCLA are well theorised. Sociocultural theory emphasises context, and discourse “in action” in its analysis. If DCLA wishes to ground itself in such theory, work will need to be done to address these aspects of discourse in computational analysis. Given the potential of DCLA to provide support for educational talk – an important aspect of learning – research should be conducted to further develop DCLA approaches to such talk.

Prinsloo, P. and Slade, S. (2013). An Evaluation of Policy Frameworks for Addressing Ethical Considerations in Learning Analytics. Proc. LAK13: 3rd International Conference on Learning Analytics & Knowledge, 8-12 April 2013, Leuven, Belgium (ACM: New York). Open Access Eprint: http://oro.open.ac.uk/36934

Higher education institutions have collected and analysed student data for years, although the purpose has largely been focused on reporting and management needs. A multitude of institutional policies exist which set out in broad terms the purposes for which data will be used and how data sets will be protected. The growing advent of learning analytics as a powerful means to utilise student data to improve both learning and throughput has seen the uses to which student data is put expanding rapidly. It is fair to say though that the policies which set out institutional use of student data have not kept pace with this change.

Learning analytics can offer real-time insights into individual students‟ trajectories which can significantly impact on their learning experiences and chances of success. Institutional policy frameworks should provide not only an enabling environment for the optimal and ethical harvesting and use of data, but also clarify who benefits and under what conditions, establish conditions for consent and the de-identification of data, and address issues of vulnerability and harm. A directed content analysis of the policy frameworks of two large distance education institutions shows that current policy frameworks do not facilitate the provision of an enabling environment for learning analytics to fulfil its promise.

Schreurs, B., Teplovs, C., Ferguson, R., De Laat, M. and Buckingham Shum, S. (2013). Visualizing Social Learning Ties by Type and Topic: Rationale and Concept Demonstrator. Proc. LAK13: 3rd International Conference on Learning Analytics & Knowledge, 8-12 April 2013, Leuven, Belgium (ACM: New York). Open Access Eprint: http://oro.open.ac.uk/36891

Social Learning Analytics (SLA) are designed to support students learning through social networks, and reflective practitioners engage in informal learning through a community of practice. This short paper reports work in progress to develop SLA motivated specifically by Networked Learning Theory, drawing on the related concepts and tools of Social Network Analytics and Social Capital Theory, which provide complementary perspectives onto the structure and content of such networks. We propose that SLA based on these perspectives needs to devise models and visualizations capable of showing not only the usual SNA metrics, but the types of social tie forged between actors, and topic-specific subnetworks. We describe a technical implementation demonstrating this approach, which extends the Network Awareness Tool by automatically populating it with data from a social learning platform SocialLearn. The result is the ability to visualize relationships between people who interact around the same topics.

Simsek, D., Buckingham Shum, S., Sándor, Á., De Liddo, A. and Ferguson, R. (2013). XIP Dashboard: Visual Analytics from Automated Rhetorical Parsing of Scientific Metadiscourse. 1st International Workshop on Discourse-Centric Learning Analytics. LAK13: 3rd International Conference on Learning Analytics & Knowledge, 8 April 2013, Leuven, Belgium. http://oro.open.ac.uk/37391

A key competency that we seek to build in learners is a critical mind, i.e. ability to engage with the ideas in the literature, and to identify when significant claims are being made in articles. The ability to decode such moves in texts is essential, as is the ability to make such moves in one’s own writing. Computational techniques for extracting them are becoming available, using Natural Language (NLP) processing tuned to recognize the rhetorical signals that authors use when making a significant scholarly move. After reviewing related NLP work, we introduce the Xerox Incremental Parser (XIP), note previous work to render its output, and then motivate the design of the XIP Dashboard, a set of visual analytics modules built on XIP output, using the LAK/EDM open dataset as a test corpus. We report preliminary user reactions to a paper prototype of such a novel dashboard, and describe the visualizations implemented to date. We conclude with a summary of potential design refinements, learning platform integrations, and user evaluations.

Van Labeke, N. Whitelock, D., Field, D., Pulman, S. and Richardson, J. (2013). OpenEssayist: Extractive Summarisation & Formative Assessment of Free-Text Essays. 1st International Workshop on Discourse-Centric Learning Analytics. LAK13: 3rd International Conference on Learning Analytics & Knowledge, 8 April 2013, Leuven, Belgium. http://www.solaresearch.org/events/lak/lak13/dcla13

OpenEssayist is a system which is currently under development. It aims to provide an effective automated interactive feedback system that yields an acceptable level of support for University students writing summative essays. The Natural Language processing techniques currently employed are keyword and key phrase extraction. OpenEssayist will be piloted in September 2013 with OUUK students following a Master’s course of study.

Wolff, A. Zdrahal, Z., Nikolov, A. and Pantucek, M. (2013). Improving Retention: Predicting At-Risk Students by Analysing Clicking Behaviour in a Virtual Learning Environment. Proc. LAK13: 3rd International Conference on Learning Analytics & Knowledge, 8-12 April 2013, Leuven, Belgium (ACM: New York). Open Access Eprint: http://oro.open.ac.uk/36936

One of the key interests for learning analytics is how it can be used to improve retention. This paper focuses on work conducted at the Open University (OU) into predicting students who are at risk of failing their module. The Open University is one of the worlds largest distance learning institutions. Since tutors do not interact face to face with students, it can be difficult for tutors to identify and respond to students who are struggling in time to try to resolve the difficulty. Predictive models have been developed and tested using historic Virtual Learning Environment (VLE) activity data combined with other data sources, for three OU modules. This has revealed that it is possible to predict student failure by looking for changes in user’s activity in the VLE, when compared against their own previous behaviour, or that of students who can be categorised as having similar learning behaviour. More focused analysis of these modules applying the GUHA (General Unary Hypothesis Automaton) method of data analysis has also yielded some early promising results for creating accurate hypothesis about students who fail.

PhD opening: Scaffolding Citizen Science with Learning Analytics

3yr PhD grant: Scaffolding Citizen Science with Learning Analytics

A PhD funded by the UK Open University’s £1M Wolfson OpenScience Laboratory
in collaboration with the Knowledge Media Institute (KMI)
3 year fully-funded PhD (Oct. 2012-Sept.2015)
Stipend: £40,770 (£13,590/year tax free)
Supervisors: Simon Buckingham Shum and Anna De Liddo
DEADLINE: 3 AUGUST

This PhD project is a unique opportunity for a highly motivated candidate. The Open University is developing the OpenScience Laboratory, an international virtual laboratory for practical science teaching, funded through a £1m grant awarded by the Wolfson Foundation. You will be working with other OpenScience PhD students on this ground-breaking project, as well joining the wider doctoral communities in learning analytics, knowledge media, educational technology, computing and science education.

The Citizen Science Challenge: Fostering Scientific Communication. A key driver for citizen science is participants’ passion and curiosity to engage with scientific questions. Often, however, citizens are deployed in projects with professional scientists in the roles of simple data gatherers/sensors, and classification. A more ambitious Open Science+Open Learning vision, however, sees citizens learning about and then engaging with other aspects of the scientific ecosystem. The specific focus of this PhD will be to break new ground in learning technologies to promote reflecting, thinking and communicating scientifically in the emerging Science 2.0 landscape.

The Scale Challenge: Timely Feedback. It is widely acknowledged that the global need for learning cannot be met by conventional bricks+mortar educational institutions, with expert mentors providing personal attention. The only way that we can collectively learn fast enough to tackle the planetary-scale challenges facing us is through the judicious use of learning technologies, amplifying and amplified by human interaction. Timely feedback to learners is known to be critical for rapid progress. This makes the use of Learning Analytics that provide automated feedback – to be reflected on, discussed and challenged by learners – a key piece of the strategy for Web-scale deep learning infrastructures.

The Convergence: This PhD will use the world class facilities of the Wolfson OpenScience Lab, coupled with the emerging tools of learning analytics, to tackle a specific aspect of the citizen science challenge introduced above: Can we develop citizen scientists’ everyday language and ways of thinking and writing, into scientific discourse? The strategy here is to help citizens engage firstly in science blogging (a semiformal genre of reflective writing), and then in longer forms of writing, such as a report on their use of the Open Science Lab. Discourse-centric learning analytics will provide feedback on the extent to which they are identifying missing knowledge, and making evidence based arguments and claims, which are the hallmarks of scientific discourse. In order to answer the core question, you will be integrating discourse analytics into the KMI tools, so that the analytics can be rendered back to users in intuitive ways which will be systematically evaluated. An interest in social computing might further focus on how citizens can be helped to share and discuss the analytics they are receiving, building a collective resource for learning about forms of Science 2.0 collaboration and discourse.

Technologies. The PhD will build on existing OU research platforms for collective sensemaking (Cohere, Evidence Hub, SocialLearn, EnquiryBlogger), and the work of Ágnes Sándor at Xerox, on the Xerox Incremental Parser (XIP) whose rhetorical analysis is capable of identifying the forms of writing that are ‘signatures’ of scientific discourse.

The Successful Candidate will bring programming skills enabling rapid Web development  (KMI tools use PHP/JavaScript/SQL). You will ideally already have a solid grounding in one or more relevant disciplines, e.g. HCI, CSCW, Learning Sciences, Web Semantics, Computational Linguistics, Discourse Analysis. Depending on your skillset, in your application you might propose shifting the emphasis to play to your strengths.

Supervision Team. This PhD will be led by Simon Buckingham Shum, who is actively researching Learning Analytics and Computer-Supported Argumentation, Anna De Liddo who works closely with Simon, with computational linguistics input from Ágnes Sándor at Xerox Research Centre Europe (Grenoble). You will also be part of the newly formed global virtual lab for Learning Analytics students, SoLAR Storm.

How to apply: DEADLINE 3 AUGUST

Please submit an outline document (max 2 pages) with your application. You need to demonstrate that you have grasped the challenge, and can relate this to the core ideas in the publications below. If you can add a new perspective from your own or others’ work even better. This is fundamentally a test of your ability to quickly marshall the arguments and write clearly, so don’t worry that this will be cast in stone. You will spend the first 9 months of the PhD developing a detailed proposal that you must defend in order to continue.

Follow the application details on the OpenScience website. You may call Simon on +44 (0)770 212 5734 or Anna De Liddo +44 (0)1908 653591 for an informal chat if you wish, or email s.buckingham.shum <usual sign> gmail dot com or a.deliddo <usual sign> gmail dot com

Key References

Cohere + XIP integration:

De Liddo, A., Sándor, Á. and Buckingham Shum, S. (2012). Contested Collective Intelligence: Rationale, Technologies, and a Human-Machine Annotation Study. Computer Supported Cooperative Work, 21, (4-5), pp. 417-448, DOI: 10.1007/s10606-011-9155-x. Open Access Eprint: http://oro.open.ac.uk/31052

KMI platforms for collective sensemaking:

Buckingham Shum, S. (2008). Cohere: Towards Web 2.0 Argumentation. Proc. COMMA’08: 2nd International Conference on Computational Models of Argument, 28-30 May 2008, Toulouse, France. IOS Press. Open Access Eprint: http://oro.open.ac.uk/10421

De Liddo, A., Sándor, Á. and Buckingham Shum, S., McAndrew, P. and Farrow, R. (2012). The open education evidence hub: a collective intelligence tool for evidence based policy. Proc. Cambridge 2012: Joint OER12 and OpenCourseWare Consortium Global 2012 Conference, 16 – 18 April 2012, Cambridge, UK. Open Access Eprint: http://oro.open.ac.uk/33253

Ferguson, R. and Buckingham Shum, S. (2012). Towards a Social Learning Space for Open Educational Resources. In: Okada, A., Connolly, T. and Scott, P. (Eds.), Collaborative Learning 2.0: Open Educational Resources. Hershey, PA: IGI Global, pp. 309–327. Open Access Eprint: http://oro.open.ac.uk/33457

Ferguson, R., Buckingham Shum, S. and Deakin Crick, R. (2011). EnquiryBlogger – Using widgets to support awareness and reflection in a PLE setting. In W. Reinhardt, & T. D. Ullmann (Eds.), 1st Workshop on Awareness and Reflection in Personal Learning Environments, PLE 2011 Conference, UK. Open Access Eprint: http://oro.open.ac.uk/30598

XIP:

Lisacek, F., Chichester, C., Kaplan, A. and  Sándor, Á. (2005). Discovering Paradigm Shift Patterns in Biomedical Abstracts: Application to Neurodegenerative Diseases. First International Symposium on Semantic Mining in Biomedicine, Cambridge, UK April 11-13. Open Access Eprint: http://www.xrce.xerox.com/Research-Development/Publications/2005-0065

Sándor, Á. (2007). Modeling Metadiscourse Conveying the Author’s Rhetorical Strategy in Biomedical Research Abstracts. Revue Française de Linguistique Appliquée, Vol. XII, (2), pp. 97-109. Open Access Eprint: http://www.xrce.xerox.com/Research-Development/Publications/2007-0295

 

Collective Intelligence in Organizations

Congratulations to Antonietta Grasso and Gregorio Convertino, who have edited a double Special Issue of the leading Computer Supported Cooperative Work journal, just out:

Collective Intelligence in Organizations: Tools and Studies

I’m pleased to say that KMi’s research into Contested Collective Intelligence, using Cohere as our social-semantic discourse platform to explore these ideas, is part of this issue, from our joint work with Ágnes Sándor at Xerox, who spent time here as an OLnet Fellow. There’s an accompanying video as well from the CSCW 2012 conference.

De Liddo, Anna; Sándor, Ágnes and Buckingham Shum, Simon (2012). Contested Collective Intelligence: rationale, technologies, and a human-machine annotation study. Computer Supported Cooperative Work, 21, (4-5), pp. 417–448. Eprint: http://oro.open.ac.uk/31052

If you share our interest in platforms for very large scale deliberation/argumentation, then you should also check out Mark Klein’s article (MIT Center for Collective Intelligence), in which he describes what we would call discourse analytics.

Finally, Gregorio chaired the most recent workshop in the international series fro mwhich this special issue emerges: Large-Scale Idea Management & Deliberation Systems

 

New: Argument & Computation journal

The inaugural issue of Argument & Computation just dropped in the pigeonhole, which is a delight to see… and there’s open access to the first issue.

Congratulations to the editors — a great milestone to see as the field matures.

“Argument and Computation aims to promote the interaction and cross-fertilisation between the fields of argumentation theory and computer science. It will be of interest to researchers in the fields of artificial intelligence, multi-agent systems, computer science, logic, philosophy, argumentation theory, psychology, cognitive science, game theory and economics. Topics of interest include, but are not limited to:

  • Argumentation and cognitive architectures
  • Argumentation and computational game theory
  • Argumentation and defeasible reasoning
  • Argumentation and nonmonotonic logics
  • Argumentation and Decision Theory
  • Argumentation and Logic Programming
  • Argumentation and game semantics
  • Software for teaching argumentation skills
  • Argumentation-based interaction protocols
  • Argumentation-based semantics of programs
  • Argumentation in natural language processing
  • Argumentation in human computer interaction
  • Argumentation in multi-agent systems
  • Computational models of natural argument
  • Dialogue games and conversation policies
  • Dispute resolution and mediation systems
  • Electronic democracy and public deliberation
  • Legal and medical applications
  • Models of bargaining and economic interaction
  • Reasoning about action through argumentation
  • Computational tools for argumentation support

In order to distinguish itself from the competition, the new journal will not solicit papers that are wholly within the theory of argumentation without application (practical or theoretical) with in artificial intelligence or computer science, nor will it solicit computational work that fails to employ argumentation as a core focus.

The journal will accept full articles, describing novel theoretical or applied research in any of the areas of interest; reviews, condensing and critiquing an appropriate subfield of research; system descriptions, focusing on implementations (typically offering online access or downloadable code) and letters, providing pithy polemic on burning issues.”

AIF 2.0: human-centred thoughts

The Argument Interchange Format (AIF) is a community effort to agree a formal lingua franca for computer-supported  argumentation. The first version is presented in Knowledge Engineering Review.

I spent 2 days last week in Glenshee, at an EPSRC-sponsored workshop bringing together a diverse group of researchers working on computing and argumentation (conference/journal). Working from 9am-8pm for 2 days solid (!), our mission was to write a report drafting AIF 2.0. This was all “thanks” to Chris Reed who runs the ARG at Dundee U, specifically his Dialectical Argumentation Machines project, who did a great job as convenor and taskmaster 😉

One of the novel aspects of this gathering is that in contrast to AIF 1.0, which was driven by the needs of computer science and AI perspectives on argumentation, they also invited “less formal” people like Tim van Gelder, David Price and me, whose tools are semiformal at most, but are (correspondingly) achieving more rapid uptake, since we do not have to worry so much as our AI colleagues about the hygiene of our representations for formal processing (the price we pay is that it is much harder to automatically evaluate our dialogue/argument maps — we leave that to human analysts scaffolded by the visual language).

As a contribution to the forthcoming report, I wrote a use case (PDF) which sews together a number of the dialogue and argument mapping tools represented at the workshop (Compendium, Cohere, Debategraph, Rationale, Carneades), which centres on an imaginary team of analysts working on the legitimacy of the 2003 Iraq invasion.

Below I’ve also pasted my position statement, and an introductory piece on approaching the design of something like AIF through a human-centred design lens.

Musings (Position Statement) for AIF 2.0 workshop :-)

(this is turning in to a bit of a live blog where I’m recording thoughts)

My work is at the intersection of Human-Computer Interaction, Design Rationale, Hypermedia, Sensemaking and Learning. I’m interested in how we foster argumentation skills from children in school (Okada and Buckingham Shum, 2008), onwards.

Working closely with US colleagues, I lead the Hypermedia Discourse project at the Open University’s Knowledge Media Institute, which has developed two open source mapping/modelling tools, Compendium and Cohere, both of which support the Issue-Based Information System (IBIS) scheme, a simple notation compared to AIF. IBIS and our tools are designed not, in the first instance, for argument modellers, but for analysts and facilitators interested in augmenting human cognition for inquiry into wicked problems through dialogue and debate. They are open-ended visual modelling tools, so can be used in tandem for many other kinds of modelling, and software tool.

Significant work has gone into studying the literacies and practices that emerge around Compendium’s usage, which helps us move it from being a raw technology to a genuine tool with a community of practice sharing experiences about its deployment in authentic contexts, such as Dialogue Mapping, Conversational Modelling, and Knowledge Art (Selvin, Buckingham Shum & Aakhus, In Press).

Technically, Compendium is designed to be as open and extensible as possible to integrate with other tools and software agents (Buckingham Shum, et al 2006; Sierhuis and Buckingham Shum, 2008; Buckingham Shum, et al, submitted). As a web application, Cohere supports many of the common kinds of Web 2.0 interoperabilities we now expect (Buckingham Shum, 2008). Recent work Dialogue Mapping the UK Election TV debates illustrates Compendium (video annotation) and Cohere.

The IBIS constructs of Arguments supporting/challenging Positions, which respond to Issues employs very basic argumentative moves seen in many other tools. As far as I know, IBIS inventor Horst Rittel did not know about any other argumentation schemes: these constructs just make intuitive sense.

We have built on Reed and Walton’s work, to convert the critical questions associated with argumentation schemes, from Araucaria AML formatted XML into visual argumentation scheme templates in IBIS (Buckingham Shum and Okada, 2008). We use the argumentation scheme templates to “explode” and interrogate the implicit substructure sitting behind those simple links. This illustrates the use of IBIS as an intuitive language in which to elicit or express more complex notational schemes.

IBIS has become something of an interlingua for researchers interested in more structured online deliberation platforms (Debategraph; MIT‘s Deliberatorium; overview; demos podcast). Ongoing work with these other IBIS platforms seeks a common format (IBIS, 2010), for precisely the same reasons that AIF was conceived: the IBIS-interoperability challenge is a microcosm of the AIF challenge, and we haven’t even cracked IBIS (!) due to the different flavours and platform-specific nuances. It would make sense for this IBIS effort to align, or be somehow interoperable, with, or expressable in AIF. But the devil is in the detail. If we could express IBIS in AIF, would we use AIF? We are musing at present on whether IBIS is in fact in a different space from AIF, a simple abstraction of AIF, or a subset of AIF. One might use AIF to unpack the implicit nature of the “supports” or “challenges” link in an IBIS structure, as described above.

So, a thorny question to lob in is whether pair-wise mappings between systems is the pragmatic approach, rather than an all-encompassing, context-free ontology. These two orientations are reflected somewhat in the early “Web 2.0 vs Semantic Web” tension: a carefully engineered ontology is powerful, but also brittle if the world being modelled unexpectedly violates assumptions (implicit or explicit). When I want to import your arguments into my system, for a particular purpose, with a given user community, on a specific timescale, in order to provide the following user experience, I might be tempted to just write the convertor tuned to those constraints rather than wrestle with a hugely expressive, but also possibly insufficiently expressive, ontology written by someone else. It’s the old code (non) re-use thing again.

I am not a knowledge representation expert, so see my contribution as being not so much at the detailed level of getting AIF notation right, but in thinking about how it might be visualized, and how usable it would be by different stakeholders who would benefit from it. Clarifying exactly who our user communities are will be important, if we are interested in seeing widespread adoption, rather than “only” defining a unifying conceptual framework (which is of course valuable in its own right for academic purposes — arguably a necessity — but I suspect we have pragmatic ambitions as well in terms of seeing it add value to practical systems).

A closing question I am musing on is whether AIF should somehow be designed to add value to representations of the Social Web. Yes, we want to inject rationality into debates, and raise the level of discourse wherever possible. Argument analysis can also be done in a completely depersonalised manner, by a third party removed from the protagonists (although any pretence to complete objectivity in the normative reconstruction of moves is of course a fiction). But if we’re developing argumentation platforms as a medium in which to engage in discourse, then argumentation is also fundamentally an interpersonal activity. There is now an active research field and industry designing analytics that show online participants’ activity levels, social connectedness, reputation, and so forth. Argumentation platforms (and by extension AIF) should help us define a semantically grounded discourse layer on top of these quantitative and graph-based indices, so that we can profile participants based on the ways in which they engage: can we imagine “challengers” (lots of challenging kinds of moves and exposing of implicit premises), “metaphorical thinkers” (lots of arguments by analogy), etc…? We have begun to ponder this in our group by examining existing social visualization tools, and asking how they would be tuned to argumentation platforms.

References

Buckingham Shum, S. (2008). Cohere: Towards Web 2.0 Argumentation. 2nd International Conference on Computational Models of Argument, 28-30 May 2008, Toulouse. IOS Press: Amsterdam. http://oro.open.ac.uk/10421/

Buckingham Shum, Simon; Selvin, A.M.; Sierhuis, Maarten; Conklin, Jeffrey; Haley, C.B. and Nuseibeh, Bashar (2006). Hypermedia support for argumentation-based rationale: 15 years on from gIBIS and QOC. In: Dutoit, A.; McCall, R.; Mistrik, I. and Paech, B. eds. Rationale Management in Software Engineering. Berlin: Springer-Verlag, pp. 111–132. http://oro.open.ac.uk/3032/

Buckingham Shum, S., Sierhuis, M., Park, J. and Brown, M. (submitted). Software Agents in Support of Human Argument Mapping.

Buckingham Shum, S. and Okada, A. (2008). Knowledge Cartography for Controversies. In: Knowledge Cartography, (Eds.) Okada, A., Buckingham Shum, S. and Sherborne, T. Springer: London. http://oro.open.ac.uk/11768/

IBIS Interchange Specification: http://projects.kmi.open.ac.uk/hyperdiscourse/docs/IBIS-0.1.pdf

Okada, A. and Buckingham Shum, S. (2008). Evidence-Based Dialogue Maps as a research tool to evaluate the quality of school pupils’ scientific argumentation. International Journal of Research and Method in Education, 31(3), pp. 291–315. http://oro.open.ac.uk/11773/

Selvin A. (1999) Supporting Collaborative Analysis and Design with Hypertext Functionality. Journal of Digital Information, 1 (4): http://journals.tdl.org/jodi/article/view/jodi-17/15

Selvin, A., Buckingham Shum, S.J. & Aakhus, M. (2010). The Practice Level in Participatory Design Rationale: Studying Practitioner Moves and Choices. Human Technology Journal. Preprint: http://oro.open.ac.uk/20948/

Sierhuis, M. and Buckingham Shum, S. (2008). Human-Agent Knowledge Cartography for e-Science. In Knowledge Cartography. (Eds.) Okada, A., Buckingham Shum, S. and Sherborne, T. Springer: London. http://oro.open.ac.uk/20301/

User-centred design perspectives

A user-centred perspective to the design of a formalism such as AIF focuses attention on questions such as:

  • What is AIF’s value proposition?
  • Who are the stakeholders?
  • What are the implications of designing for a heterogenous mix of users and platforms?
  • How does AIF fit into the real world activities, tools and intellectual capabilities of the range of envisaged users who are either conducting argument analyses based on material, or engaging in argumentation with each other?

When we consider the socio-technical “ecosystem” of argumentation platforms and users, it is clear that it is heterogeneous: diverse kinds of users and platforms, with different kinds of capability. This has consequences, for instance:

  • The passing of data will not be automatic in all cases. Only “one-way” interchange will be possible from more expressive to less expressive platforms. Data from less expressive platforms will require human intervention to provide missing details before more expressive platforms can add value to the analysis.
  • There is also the possibility that AIF-interoperability between two systems will fail, not because of limitations on AIF’s part, but because the two systems are in principle incompatible. This might be because the recipient platform requires data that the source cannot provide, or because they have incompatible models of argumentation. The introduction of a mediating representation such as AIF will not help in such situations.
  • With “users” ranging from members of the public (e.g. sharing their views in a public debate), to students, to forum moderators, to software developers, to academic argumentation researchers, we are dealing with a vast range of computing expertise, familiarity with argumentation theory, and tasks. To what extent will this shape AIF?

The insights from sociologists of science, and in particular, of how software and data standards are specified (e.g. Bowker and Star, 1999), provide intruiging accounts of the diverse constraints that lead to a given model. These are often compromises: when a diverse community seeks to agree a standard, they are seeking to satisfy many intersecting and sometimes conflicting goals. Ensuring compatibility with one’s own research interests may be in tension with the needs of developers or end-users. The AIF 2.0 standard, like any other, will require not only technical elegance, but a sufficiently aligned mission, and spirit of collaboration that has taken us this far.

Value proposition (VP)

The design of a new “product” must provide a compelling value proposition:

  • What problem is solved?
  • How much does it cost? — in every sense of the word: e.g. money to employ suitably skilled staff; learning curve to use successfully; compatibility with existing technologies and methods…)
  • Who will pay?

What is AIF’s value proposition? To help us learn by analogy, we might challenge ourselves to complete this:

AIF is to < beneficiary community > as

<another model/standard> is to < success story beneficiary community >

We will consider possible answers to this as we consider the possible beneficiaries.

Academic audience

One audience for AIF is the community developing it: researchers in the field that has come to be known as Computational Modelling of Argument (COMMA) are developing it to solve commonly felt problems.

An obvious value proposition question is: Why has AIF 1.0 not taken off and established an active user community already? Consultation with AIF workshop participants identified the following factors:

  1. There was no usable reference implementation, only paper specifications, plus a few small prototypes
  2. It was not expressive enough for COMMA researchers
  3. There was no engagement with communities building argument mapping platforms, who do have real user communities, and data to share. Moreover, it was too complicated for non-COMMA researchers to grasp.
  4. A significant proportion of the COMMA community is working with abstract frameworks such as Dung’s, which has a very different model of argumentation from naturalistic argument, and which does not have significant databases of arguments: cross-platform data exchange was not a pressing problem for these researchers whose focus is on the formal properties of argumentation reasoning engines.

Factor 1 (reference implementation) we hope will be addressed as developers work to the draft specification. Factor 2 is addressed through a modular approach to adding in ontologies of importance to more formal COMMA researchers, and Factor 3 through the involvement in AIF 2.0’s design of argument mappers who have significant user communities (see the use case in the appendix). Factor 4 should begin to be addressed by AIF 2.0, which we hope will make it easier for theoretical researchers to obtain substantive datasets from researchers who have material on their platforms.

Continuing the focus on value proposition, it has been argued that apart from being an interchange format, AIF also provides a theoretical sounding board against which researchers can compare and contrast their approaches. Clearly, researchers already have established ways to do this: the argument is that AIF seeks to add a new kind of value by requiring integration at a much finer granularity and precision. For instance, we can now move beyond agreeing that two approaches are “broadly compatible” or “inspired by the same argumentation model”: AIF invites researchers to prove this very concretely, and to learn from the successes and failures of attempting to do so.

Developers

Software developers building systems to model and/or mediate argumentation, will welcome AIF if it saves them time, or leads to more elegant or efficient systems. They will be looking for clean code, modular architecture, solid APIs, etc. But they in turn will be paid (often) by solution providers who (should) bring to bear the end-user requirements.

Solution providers

From a Human-Computer Interaction/Design orientation, solving real world problems is the key driver.  The mission is to add value to people’s lives, including getting an existing job done more efficiently, getting new kinds of jobs done that weren’t possible before digital tools, and enhancing the aesthetic dimension to life.

Several argument mapping groups have spent a long time designing, trialling, evaluating (and pitching to funders) the benefits of informal/semiformal argument structuring tools. We are teaching pupils, students, researchers and other professionals to engage in the normative reconstruction of naturalistic spoken and written discourse, from which they then gain personal and group cognitive benefit. We face new literacy challenges when it comes to reading and writing dialogues and arguments in more structured ways. Who exactly will use the tools?

User-centred strategies/perspectives

Start with success stories: One strategy for deciding what should be in or out of AIF is to examine whatever we might consider to be our biggest success stories in computer-supported argumentation (broadly defined), and ask what should be learnt, if/how they could have been even better with AIF, or how AIF (as an encapsulation of the community’s collective intelligence) would help propagate key design advances/patterns. We have not yet had the chance to do this.

Design challenges: Another strategy (either in the absence of, or in addition to real success stories) is to compete to engineer systems solutions to challenges which the community agrees are meaningful (cf. Robot Olympics). Solution quality drives this, with a given theoretical/academic perspective forced to justify itself in specific contexts. Understanding those patterns is then extremely important to provide the frameworks preventing others from making the same mistakes.

Argument modelling challenges: Apart from engineering challenges, we might also have argument modelling challenges, such as agreed texts that need to be modelled either manually by analysts, using a given tool, or automatically by approaches using computational linguistics (here TREC is the obvious analogy to draw). An initial experiment along these lines was GlobalArgument.net (GA.net).

Dominant user interaction paradigms: Another approach starts by reviewing what are the most compelling modes of usage in argument mapping tools and online deliberation platforms, and deriving requirements from those that AIF must support. Examples would be:

  • Document annotation (typically Web) is found in Cohere and OVA, Araucaria supports offline annotation, and other Compendium and bCisive support linking to external files/URLs. AIF must support the notions of:
    • restating/paraphrasing/quoting, grounding nodes in the argument network in external sources, and better, in specific locations in those sources
    • distinguishing the authors of those argument nodes from the authors of original sources.
    • Task-specific argumentation: people always conduct argument analysis or engage in argumentation in a context, and domain-specific tools tend to do better (eg. specialising the notation to the language of the domain, integrating with other technologies in that domain’s ecosystem). What are the implications for AIF?

Use cases: Use cases seek to envisage who would use AIF-interoperable tools, and what benefit they would gain, in order to clarify not only the vision of the overall effort, but also identify any requirements on AIF. Examples include the following:

  • Exporting AIF from different platforms, and importing it into an AIF-compatible network visualization package. We assume that these are better than can be provided by passing a generic graph viz package generic graph data. (This might be smoothly delivered via web services etc, should this prove feasible in principle)
  • Exporting AIF enables more powerful statistical NLP than is possible on an unstructured corpus.
  • AIF aggregators, thus enabling searching across argument databases.
  • Use of AIF as structured markup to claim trustworthiness

IMPACT Research Fellow, Leeds

Great job going with some of the top people in computer-supported argumentation and e-democracy (collaborative project funded by the European Commission: Fraunhofer, University of Amsterdam, University of Liverpool, and two companies specialising in user interface design (User Interface Design GMBH) and online consultations (Zebralog GmbH & Co KG)…

The Centre for Digital Citizenship, Institute of Communications Studies (ICS)

Research Fellow for IMPACT: Integrated Method for Policy Making Using Argument Modelling and Computer Assisted Text Analysis

ESSENCE workshop podcasts

Just a note to say the podcasts from our ESSENCE project workshop are now up, allowing you to watch some of the leaders in the field of online argumentation and deliberation present updates of their work and demo their systems… Jack Park, David Price, Mark Klein, Luca Iandoli, Aldo de Moor and Anna De Liddo

essence-podcasts

HypER 2009: Hypotheses, Evidence & Relationships

You know that feeling that you’ve come home? I recently joined a 2-day workshop at Elsevier’s Disruptive Technology Labs, Amsterdam, where an exciting group of people shared what is clearly a harmonic convergence that’s been waiting to happen for a long time. HypER: Hypotheses, Evidence & Relationships is the name we gave ourselves. From the nascent wiki homepage:

The next step in the evolution of the digital research infrastructure is the detection, navigation and analysis of  Hypotheses, Evidence & Relationships in the literature: the building blocks of knowledge-level claims.

This community brings together researchers in argumentation, computational linguistics, sociology of science, hypermedia, semiotics, semantic and pragmatic web.

Check out the participants and presentations from Day 1, and the ensuing activity streams to take this forward… such as intersection with the W3C Scientific Discourse working group.

What makes this particularly exciting from the perspective of our Hypermedia Discourse group is that we’ve been working since 1998 on tools and theory for human-annotation of discourse relations, while meantime, the machines have been getting smarter at extracting certain forms of these from traditional scientific texts. Now, as I argued in my presentation, there’s potentially a marriage made in heaven for us to really move the knowledge infrastructure forward, out of the shadow of the printing press, into a network-native environment that takes seriously the power of the social/semantic web. Moreover, it takes seriously the pragmatic level of communication by recognising the rhetorical role that different statements play in scientific communications.

My specific argument on the human+machine annotation synergy is that no matter how smart the machines get, we will always need human annotation of hypotheses, evidence, and discourse relationships, and very high quality user interfaces (Slide 10):

Researchers read meanings into texts that are not there, and with which the author might disagree

  • so we will always require manual annotation tools
  • we need ways to make connections to connections
  • extremely complex connections may remain the province of human sensemaking (e.g. is analogous to)

Good user interfaces will be needed

  • to view, edit and navigate HypERnets, whether manually or automatically constructed

Scientific discourse is a social process

  • we take huge care in our writing about how we position ourselves in relation to our peers — will we trust unsupervised machines to extract and position our more complex claims?
KMi HypER 2009

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