Dear Data One of the things I’ve been fairly excited about recently
is the ‘[dear data]1‘ project, which is an “analogue data drawing project” between two women who’d “swapped continents” via postcard. Each week they both draw by hand a representation of some data-theme for that week.
Dear Data is live! http://t.co/x95vhckhUr a yearlong, analog data-drawing project by @giorgialupi and @stefpos pic.twitter.com/POg4cl3NYJ
— Dear Data (@_deardata) March 19, 2015
I went to see [Stefanie Posavec]2 talk at the Open Data Institute on Friday (it should be up on [their youtube channel]3 eventually), including on the dear-data project. Through a lot of her work she noted the kind of performativity of the information visualisation – in some cases real performance/physicality e.g. an [open data playground]4.
Turning data into objects of beauty and play makes it real for people. @stefpos‘s #blog/opendata playground: http://t.co/MvD7At7tNd #blog/odifridays — Ellen Broad (@ellenbroad) July 24, 2015
Their process is to take a topic each week, and then in parallel collect data about the topic (but not necessarily the same types of data), only creating the visualisation at the end of the week (and again, these differ). So inevitably, the collection and visualisation of the data itself is a performance, and has an impact on behaviour.
Data is the beginning of the story, not the end http://t.co/bt0LUFECs9 #blog/odifridays @stefpos
— Simon Knight (@sjgknight) July 24, 2015
Dear learner? So I reckon it’d be fun to do something on this around
learning data. I’m not sure how pretty it’ll be 🙂 (the dear-data 2 are both information designers!) but I think the concept of ‘dear learner’ is a nice one. For example, this could be based around the kinds of questions we’re interested in/ask when we look at learning as researchers, but casting us as learners too. Obviously it doesn’t have to be a week of data, it could be a single episode. I think it’d be cool to have a representation, plus something about the learning aspect, and maybe also a little piece on how the data was collected (maybe even some code snippets!). I guess there’s something nice here about thinking about what kind of data is available for some questions, and being driven from that direction (and the inevitable flaws)…but it might be way way way too complicated (not to mention, I don’t think I own a pencil! :/ ) There’s lots of work on visualising learning data for dashboards, and complexities of dashboards interpretable for learners, educators, and administrators (see e.g my short [zotpressInText item=”{3IT499S8}”] ([here]5)), but that research is mostly about presenting fixed data visualisations which the learner (educator or administrator) then makes sense of. (An aside, there’s a nice post from [CILIP on how people engage with data visualisations]6).
Accuracy less important. Representations of noticing (or voids-failing to notice) through hand collection of data #blog/opendata — Simon Knight (@sjgknight) July 24, 2015
#blog/opendata data gathering as personality & performativity. Counting as emotionally charged @stefpos @ODIHQ pic.twitter.com/SBdj9h0E6y
— Simon Knight (@sjgknight) July 24, 2015
I really like the idea of taking a different perspective asking how, on a micro level, we can make sense of our own data traces and how the process of personal-data-visualisation might be of interest (aside: [Nice paper on data-sketching practices]7). It takes the quantified self and learning analytics trends, and singularizes(?) them (as opposed to personalising, where we design for individuals, but it is still WE design FOR). I’ve been wondering how this ties into the work the [code acts folks]8 are doing, thinking about how many data visualisations (and other analytic feedback) provide a ‘given’ reading of what is going on that may separate the learner from their own learning (which is ultimately the data-source for such visualisations), as Ben says in [this post]9 : > digital policy instruments all conform to a realist view that education can be presented as ‘visualized facts,’ rendering visible particular representations of the data whilst rendering invisible the underlying statistical and algorithmic techniques performed on it, by new kinds of technical data experts, to make it intelligible. Of course there’s lots more to say around that, and I’m very aware that [personal informatics]10 or quantified self (and different data visualisation, [here]11 and [here]12, around that) can be seen as empowering to help individuals understand their own data-lives. But that’s part of the exploration – thinking about how far you have to go to bring people into creating, not just consuming, data vis (another aside: [platform cooperativism]13?. The correspondence element in dear-data invites the viewer to consider the possible visualisation spaces, and the visualisation semantics in the context of the learning-theme, because it invites the reader into a space of both representation possibilities and data-trace possibilities and their semantics.
Probably our favorite week so far: data-ing the sounds of our days! http://t.co/XQ5lUA7xQw #blog/DearData pic.twitter.com/Eao3ALwoLa — Dear Data (@_deardata) July 15, 2015
On the one hand I can imagine a playful critique of some of the data dashboards, and operationalisations we make in research, and as my friend [Stefan Kreitmayer]14 point out there’s something almost intimate about ‘dear’. On the other, I wonder whether there’s something interesting in looking at whether this might be a space for learners to constructively engage in, rather than being recipients of, their processed data. Of course, the dear-data artists are in fact professional designers, so this is a daunting thing(!), but there’s also something interesting about the learning features there.# What questions do we ask? So I was thinking about this (I even played with a [wordpress theme]15 & thought about a [user submission plugin]16!) and would love it if there were a feasible way to get something happening, definitely more likely crowd-sourced & curated (seriously, I checked, I don’t own a pencil) but playing with the ideas above so each month would have a theme/category & each post would have a visualisation and explanation. Anyway, I started thinking about some possible topics (and things one might look at in them) as below. I think lots could be merged/reworded so probably reduced to maybe 6 bigger ideas around the sorts of questions learning researchers are interested in. I’d love thoughts (I’d also love to turn up to LAK with a giant data-postcard as a poster!) 1. How do you know what you know? Thinking about metacognition, self regulation, epistemic cognition, etc. 1. Data could be about knowledge checking activities, looking for/soliciting (opposing) opinions, citation practices, etc. 2. Can you build knowledge with others? 1. Data could be collaborative document editing, or e.g. Critique – peer-review, or something? Or other critical evaluative discussions, knowledge building, etc. 3. Can you create an effective working space? 1. Data could be about location (café, office, library, etc.) or physical environment (e.g. im in my office now with my feet up on a sofa, earlier I was in the office chair dual screening, etc.). 4. Are you aware of your learning/Do you know what you’re working on? 1. Data could be about the kinds of things you’re reading, or engaged in? 5. Who are you learning with? 1. Data could be SNA of (academic?) contacts over a period maybe inc twitter, etc.? 6. Are you planning your learning? 1. Data could be about lots of things, e.g. to do list – something about adding and removing things from a to do list http://www.dear-data.com/week-10-1/ 7. Can you see the bigger picture? (Or narrower, things like: Can you connect stuff, do you understand connections between discipline, can you translate your work for a wider audience/impact?) 1. Data could be about times you explained something, or blogged about a paper you wrote/read, or synthesised from a number of disciplines, etc. 8. What does your writing say about your learning? 1. Data could come from different ways to visualise writing outcomes, topic, sentiment, word counts, structure, the XIP tool, etc. 9. Are you a resilient learner? (Dweck, etc., e.g. the [learning emergence]17 stuff?) 1. Do you keep going when you hit problems, e.g.???? Maybe questions you ask? 10. Have you maintained work life balance this week? 1. Anything you like here I think! 11. Are you a reflective practitioner? 1. E.g. people do stuff on reflective blogging, but it might involve doing something extra…or thinking about e.g. reports (progress reports!) and again planning based on that? Maybe some mood things? 12. Are you a professional (do you act like someone in your profession)? 1. ????? Broadly thinking about the epistemic games stuff here, I guess data around professional activities? (David Williamson Shaffer would want data on displays of: Skills, Knowledge, Identity, Values and Epistemology of the practice) 13. Are you an effective learner? 1. e.g. Time/quantity edits in documents, can you use your tools effectively? Do you go off task a lot
Footnotes
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http://www.cilip.org.uk/cilip/blog/how-people-engage-data-visualisations-why-it-matters ↩
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http://innovis.cpsc.ucalgary.ca/supplemental/Data-Sketching/ ↩
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https://codeactsineducation.wordpress.com/2015/05/05/digital-education-policy/ ↩
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http://quantifiedself.com/2010/02/results-of-the-personal-inform/ ↩
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http://learningemergence.net/about/learning-futures-design-principles/ ↩