Connecting 131008

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It has been a busy few days.

I am delighted with the response to Darrell Cobner’s guest post.

I have been thinking a great deal about the “disciplinary gaze” issues raised by Darrell and by Chris Carling and his colleagues (2013).

I believe profoundly that this gaze has agnostic qualities … it occurs in a variety of contexts and at different tempos.

Digital sharing is transforming scholarship and I hope that by connecting through a range of media we enable thick descriptions to emerge and be shared openly. I keep returning to the concept of CommentPress:

CommentPress is an open source theme and plugin for the WordPress blogging engine that allows readers to comment paragraph-by-paragraph, line-by-line or block-by-block in the margins of a text. Annotate, gloss, workshop, debate: with CommentPress you can do all of these things on a finer-grained level, turning a document into a conversation.

Whilst installing the CommentPress Core plugin for WordPress, I managed to remove all my customisations for Clyde Street! I am going to set up a new blog space to share the functionality of the plugin and explore the possibilities for co-authorship.

As I open up these opportunities, thanks to Jenny Mackness, I am mindful of the growing discussion of connectivism.

George Couros has reminded me that Isolation is now a choice educators make. He notes:

Personally, blogging has made me really think about what I do in my role as an administrator, and I would say that the process has really clarified a lot of my thinking.  The other aspect of writing for an audience and getting their feedback has made a huge difference on my learning as being challenged has made me really think about my work.  In fact, I am writing this because someone read my blog post, challenged it, and I came back to revisit my thinking.  That wouldn’t have happened if I wrote it in a journal that I tuck away at home.

When my daily feeds enable me to read about James Grayson’s work and contemplate data shared by Ted Knutson, I am excited think about what co-production might achieve.

Propsects of co-production returned me to a Dan Pontefract post from 2011. I have been thinking about how our personal learning journeys and environments move us through his Digital Learning Quadrants.

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I do think gaze is transformed by the opportunities to collaborate and cooperate. It might lead us to engage in the kind of discussion about data  Annette Markham proposes.

Data is, as research terminology goes, a deceptively easy word to toss around. It’s easily accessible for most of us, fills in as a better descriptor than the term ‘stuff,’ and adds instant credibility to that which it describes. The term ‘data’ does far more than describe units of information used in the course of one’s study. It functions as a powerful frame for discourse about knowledge — both where it comes from and how it is derived; privileges certain ways of knowing over others; and through its ambiguity, can foster a self–perpetuating sensibility that it is incontrovertible, something to question the meaning of, or the veracity of, but not the existence of.

 Photo Credit

Busy District Line (2) (Owen Blacker, CC BY-NC 2.0)

The Visual Analytic Turn

Seventeen years ago, Usama Fayyad, Gregory Piatesky-Shapiro and Padhraic Smyth wrote:

Across a wide variety of fields, data are being collected and accumulated at a dramatic pace. There is an urgent need for a new generation of computational theories and tools to assist humans in extracting useful information (knowledge) from the rapidly growing volumes of digital data. These theories and tools are the subject of the emerging field of knowledge discovery in databases (KDD).

I revisited their article in the AI Magazine this week after a number of finds prompted me to think about the visual analytic turn in sport.

The first visualisation that grabbed my attention was an English Premier League fixture strength table prepared by Neil Kellie (shared with me by Julian Zipparo). Neil used Tableau Public for his visualisation.

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Neil developed his table by using a static star rating and a form rating combined to give a score for each fixture. This becomes a dynamic table as the season progresses. It has prompted me to think about how we weight previous year’s ranking in a model.

The Economist added its weight to the Fantasy Football discussions with its post on 16 August. The post uses topological data-analysis software provided by Ayasdi to visualise Opta data on the different attributes of players. In an experimental interactive chart:

the data is divided into overlapping groups. These groups contain clusters of data—in this case footballers with similar attributes—which are visualised as nodes. Because the groups overlap, footballers can appear in more than one node; when they do, a branch is drawn between the nodes. Some nodes have multiple connections, whereas others have few or none.

Ayasdi

There is a 2m 32s introduction to the Ayasdi Viewer on YouTube. Lum et al (2013) exemplify their discussion of topology with an analysis of NBA roles. Their insights received considerable publicity earlier this year (“this topological network suggests a much finer stratification of players into thirteen positions rather than the traditional division into five positions”).

Back at Tableau Public, I found news of a Fanalytics seminar. One of the presenters at the workshop is Adam McCann.  Adam’s most recent blog post is a comparison of radar and parallel coordinate charts. Adam led me to a keynote address by Noah Iliinsky: Four Pillars of Data Visualization (46m YouTube video). Noah works in IBM’s Center for Advanced Visualization.

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This snowball sample underscores for me just how many remarkable people are in the visualisation space. I am interested to learn that a number of these people are using Tableau Public … to share sport data.

In other links this week, Satyam Mukherjee shared his visualisation of Batting Partnerships in the first Ashes Test 2013:

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Simon Gleave’s 26 Predictions: English Premier League forecasting laid bare reminded me of the discussions following Nate Silver’s analysis of the 2012 Presidential Elections. I enjoyed Simon’s juxtaposition of 26 pre-season Premier League predictions, “13 which are at least partially model based, and 13 from the media. The models select Manchester City as title favourites but the journalists favour Chelsea”. Simon’s post introduced me to James Grayson and his reflection on predictions about performance. I think Simon and James have a very impressive approach to data.

This week’s links have left me thinking about an idea I had back in 2005. I wondered at that time if I could become skilful enough to combine the insights offered by Edward Tufte and Usama Fayyad. More recently, I have been wondering if I could do that with the virtuosity that pervades Snow Fall.