Dear learner

Dear Data

One of the things I’ve been fairly excited about recently is the ‘dear data‘ 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.

I went to see Stefanie Posavec talk at the Open Data Institute on Friday (it should be up on their youtube channel 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.

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.

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 (here)), 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).

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). 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 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 :

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 or quantified self (and different data visualisation, here and here, 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?. 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.

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 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 & thought about a user submission plugin!) 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
  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 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

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This Post Has 8 Comments

  1. Simon Knight
    Simon Knight says:

    August 14th, 2015 – September 12th, 2015
    Opening: August 13th at 7 pm

    To measure, to quantify the physical and intangible dimensions of a place, is to articulate facts in order to construct values. The process of creating standards and guidelines of representation allows innovation to enter the realm of the establishment. What can be measured can be capitalized, historicized, and sold.

    While architectural representation conforms to a system of standards and guidelines that allows for the production of buildings, architecture is also the practice of giving form to thought. In the process of creating edifices that house social, political, and spatial relations, architects make visible the functions of society in operational and aspirational terms. In this sense, architecture is constantly innovating new forms of measurement and representation.

    The pleasure and pressure to measure and be measured has become increasingly present. Access to growing data sets and new sensing technologies is widespread, and the role of public and private domains in terms of information and space are being redefined. These contemporary conditions invite us to reflect on our ideologies and values, and the drawing is a manifestation of that which we are able to (and desire to) count, measure, and draw.

    Measure is an exhibition of newly commissioned drawings by 32 international architects presenting 32 edifices of thought. Drawings are of Storefront for Art and Architecture’s gallery space on 97 Kenmare Street in New York. Architectural representation, which draws upon the diagram as a conceptual and abstract component, has historically been criticized as obscure and self referential. The proliferation of data visualization in popular media today, however, allows us to engage a much larger audience in conversations about measurement and representation. The 32 drawings presented at Storefront unveil the challenges of representation and extrapolate them onto the architect’s table and the gallery walls.

    Storefront’s third iteration of the drawing show seeks to find measures, resist measurement, and measure the immeasurable by presenting drawings that range from the real to the fictional and from the functional to the symbolic. Measure positions the medium and the act of drawing as a process by which we seek coherence in data and representation, and shows that it is the making of facts that is the basis for the production of futurity beyond existing norms.


    As part of the exhibition, and on display on the facade gallery walls, Storefront has invited artists to present a series of works that use data to measure and construct new territories and architectural forms. Works range from sculptural to cartographic and from physical to digital.

    Participants include:

    Landscapes of Profit by Dan Taeyoung, Caroline Woolard, Chris Henrick, John Krauss, Ingrid Burrington
    Landscapes of Profit measures the amount of money that would be generated if a 1% surcharge or “tax” were placed on sales of flipped properties, and proposes earmarking this tax for a fund for affordable space. The project positions the recollection of data and its visualization as a form of activism.

    Wage Islands by Ekene Ijeoma
    Wage Islands expands on the narrative of New York City’s “tale of two cities” by measuring and revealing inequalities in wages/income and housing using a three-dimensional map. This project reflects the geographies of access in New York City, and seeks to reveal the inherent relationships between housing, income, and inequality.

    InSeE’ by Citygram: The Hong Park, Evan Kent, Sean Lee, Min Joon Yoo
    InSeE’, an acronym for Interactive Soundscape Environment, focuses on sonification and visualization of soundscape data captured by sensor network technology captured by the Citygram sensor network system.

    Dear Data by Giorgia Lupi + Stefanie Posavec
    Dear Data is a year-long analog data drawing project using personal data and drawings on mailed postcards to challenge the increasingly widespread assumption that “big data” is the ultimate and definitive key to unlocking, decoding, and describing people’s public and private lives.

    PLUS ONE by + POOL
    + POOL uses visualization to bring into a single graphic the gradient of data used in the process of designing and conceptualizing the project. The project incorporates scales and forms of measurement in many ways, from data regarding microscopic water pollution to assessments of the implications of improved waterways in cities worldwide.

    Insight by CartoDB: Santiago Giraldo, Aurelia Moser, Andrew Hill
    Insight is a map that builds on the ideas of abstraction, measurement, and insight through the use of precise community-created data maps overlaid onto a less distorted and more proportionate view of land masses that we typically see as skewed on more common projections. The map content is a collection of data visualizations and insight maps made by the CartoDB community, a cloud-based mapping, analysis, and visualization platform. The full interactive map can be found at

  2. […] In learning analytics contexts one of the things we’re interested in is how stakeholders – managers, educators, students, parents, etc. – interact with ‘their’ data at the various levels of granularity. Of course part of that is about how that data is represented and visualised, and the kinds of collaborative sensemaking processes that stakeholders engage in. One of the things I’m also interested in is inviting students into the processes of learning analytics and the space of data representations (per ‘dear learner’). […]

  3. Simon Knight
    Simon Knight says:

    Current efforts to build data literacy focus on technology-centered approaches, overlooking creative non-digital opportunities. This case study is an example of how to implement a Popular Education-inspired approach to building participatory and impactful data literacy using a set of visual arts activities with students at an alternative school in Belo Horizonte, Brazil. As a result of the project data literacy among participants increased, and the project initiated a sustained interest within the school community in using data to tell stories and create social change.

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