Week 6 summary: ethnographic artefact

My ethnographic artefact, a series of data visualisations, is at:

This week I spent most of my time lurking in the EdX MOOC, even if I had already finished copying the text I would analyse later for the ethnographic artefact. I felt this time was necessary to understand what was happening in the community. Although I spent most of the time reading as opposed to writing posts, I would not characterise this type of activity as passive. Observing a community requires a certain level of concentration and focus. Photo 22-02-15 15 06 23.png

This focus is also necessary because I could learn about the community’s main activity, the coaching circle, from participants’ account of it. I was therefore quite pleased to have found that the introspectiveness I observed could be revealed quite clearly via data visualisation. The other thing I discovered is that these introspectiveness among participants seemed to be a function of time. I could find this pattern only among the posts that were created in the latter half of the review period. Interestingly, while most participants had managed to form connections, and deep ones at that, with their coaching circles, questions about how to organise and join those groups persisted. That these questions remained well into the third week of the course has implications for the community organisers.

The rest of the week I posted ethnographic accounts of two large social media sites: Reddit and Instagram. What I found interesting was how the two accounts differed in their tone. While the Reddit account was dispassionate, the Instagram was quite evocative. I think the kind of role the ethnographer takes influences the account he or she creates in the end. Lastly, I created an image as a reply to Katherine’s post about non-people, that is, non-human agents in posthuman gatherings. This theme of assemblages is something I encountered in the first week of the course and gradually better understood in the second. It provides I think rich fodder for thinking about learning in the digital age.


  1. jdarling says:

    Ed, first of all, I just wanted to say how impressed I am with your ethnography. It looks beautiful, it makes me want to explore it and understand it. The visual representations help me with identifying the patterns that you have identified.
    The coaching circles are intriguing – I get the impression that they have been very successful – can you see any downside to them? Has anyone expressed any dissatisfaction? Does the self-organisation work, or do learners naturally gravitate towards others with similar views and experiences?

    • Ed Guzman says:

      Thanks Jin. Indeed the coaching circles were quite successful; however, the downside is that they were quite to difficult to organise. Half of the posts in the forum were about organising them, even three weeks after the course had started. The site uses a spreadsheet that allows participants to form groups of five, with each group assigned a number that helps participants ask others to find and join them. There were some complaints about members not showing up but none about members being antagonistic or insensitive with each other. My impression is that the course topic attracts a particular audience. Combined with the fact that the discussion forum requires an additional registration, only participants with similar levels of commitment and views end up participating in the circles.

  2. sbayne says:

    The form you’ve chosen for your artefact is really great Ed, and as usual you make excellent use of visual representation to get your point and analysis across. I particularly like the way you align the ‘from organzing to reflecting’ column chart to Kozinets’ move from ‘tasks’ to ‘relationships’. It would’ve been interesting to see more tracing of the participation patterns over time – but within the limits of this activity that is perhaps asking a bit much.

    Could you say a bit more about the final visualisation – ‘introspection’ – as it’s not quite clear to me what is going on here, or how the visual has been generated, and how it has informed your brief analysis? How does the correlation between words indicate ‘deep connection’ between participants? It would be very interesting – either here or in the final assignment – to see more analysis of how these lovely visualisations are generated, and how the textisbeautiful algorithms work on text in a particular way to achieve particular effects.

  3. Clare says:

    Wow Ed! Not only really interesting but great presentation!

    Would also love to know more about how you created the visualisations.

  4. Ed Guzman says:

    Hi Sian, Jin, Clare and Nick,

    Thanks for the comments and prompts, and apologies for the lack of explanation about how the visualisation tool works!

    The basic unit of the visualisation is not actual words but topics. textisbeautiful.net defines topics in a particular way. Unlike simple word counts (how many times does a word appear ), topics represent words or phrases that appear closely and frequently in the text. Topics are determined not by word counts but by the tool’s algorithm. Text is beautiful refers to this algorithm as “our technology” and provides some explanations about the visualizations it generates but not much information about how this algorithm works. At least that’s how I understood this!

    According to explanations provided on textisbeautiful.net, topics are then grouped according to themes. (Although as a caveat, I did see three themes that contain only one topic: software bug?) Themes are displayed differently based on the type of visualisation. In word clouds, themes are those topics that appear in the same color; in the correlation wheel, themes are the topics that appear within the same arc. Themes should be viewed collectively: each topic within the theme are logically related. A good example of how themes summarize the text is the theme that contains the topics “group”, “post”, “email”, “send”, “forward”, “subscribe”. Another good one is the theme with the topics “circle”, “time”, “join”, “looking”, “members”, “available”. Both are incomplete lists but they summarise how participants tried to organize themselves into groups, primarily via email.

    Text is beautiful provides another logical grouping, one that looks at how often a topic appears when another topic is present. Text is beautiful calls this as co-occurrence. Like a theme, co-occurence is calculated, that is, it is determined by an algorithm. Co-occurence is displayed as the highlighted lines inside the correlation wheels. For example, when the topic “silence” appears, the other topics that appear are “stillness”, “images”, “deep” and “metaphors”.

    My claim is that the co-occurence of certain topics provide visual evidence of the relationships that participants have built within the MOOC, and that I have read about in the MOOC discussion forum. First, I divided the posts within the forum into two: posts that were about how to organize groups of participants called coaching circles, and posts that were about participant’s reflections about what occurred within those groups. I noticed that the language and tone of those two kinds of posts were very different from each other. The second type, the reflective ones, were more personal, mentioning feelings and emotions more frequently. In the actual posts, several participants reported even crying and feeling deep gratitude after a coaching session. My problem was that I did not want to quote those posts, and certainly not publicly. The correlation wheels provided an abstract but evocative solution. For example, the topic “today” co-occurs with the topics “clarity” and “community”, alluding to the personal insights that participants have gained and have reported happening within the community.

    I realise this is crucial information I have missed. Again, apologies for not including this as part of the artefact.

  5. Jeremy Knox says:

    Really interesting artefact Ed, and excellent to see your descriptions in the post and further explanations in the comments. Data visualisations are so appealing aren’t they? I guess that is why I appreciated your explanation of the production process here. Visualisations have an explanatory power that I think needs to be interrogated and critiqued. We need to understand *how* visualisations produce knowledge about communities, rather than just considering the surface image.

    So, my question would be about what you think was lost and gained through representing the community in this way. A more traditional ‘ethnography’ might have generated written field notes, so do the visualisations add something more ‘objective’ or ‘evidence based’ here? And perhaps more generally, how do you view data visualisations – do they provide the new ‘truths’ about social life so often promised? Some of these themes will be great to continue thinking about as we enter the third block on algorithmic culture.

  6. PJ says:

    Ed, et.al., visualizations appeal to me, on a certain level, because I consider myself a ‘visual’ person. I know that Jeremy is an expert on all things MOOC and ‘visual.’ Did we share Tweet of Jeremy’s article “From MOOCs to Learning Analytics: Scratching the surface of the ‘visual'” (eLearn Magazine, Nov 2014)? or did I just read that article on my own? Anyway, one point I would like to add here is that I learned as much much Ed’s elaboration above and the questions and responses above as from the artefact itself. I was previously genuinely interested in the MOOC that Ed is enrolled in and further intrigued by the concept of ‘coaching circles.’ Typically, one thinks of a coach, not a group of coaches. I viewed Ed’s post earlier this week, and it aligned with an path of exploration of “meta-” leading to my discovery of Peter Diamandis’ “meta-intelligence group” which I tweeted and will blog about.
    The future of education is about collective and constructivist learning, collaboration, ‘coaching circles;’ call it what you will.

    Ed, I was also intrigued that you chose correlational wheel type visualizations. I think different people are attracted to different types of visualizations. Some people like pie, or bar, or line charts. I tried Textisbeautiful.net myself, and posted on my blog for my ScanFilmTV MOOC micro-ethnography script. I chose topic clouds for that visualization. It illustrates immediately some salient themes because I am familiar with what I am looking at. For example, ‘Danish’ and ‘Swedish’ referring to films are more prominent than ‘Norwegian.’ That was immediately evident to me, but might not be to other viewers. I did find Textisbeautiful to be more useful than other word picture tools that I have tried, such as Wordle. Your 28 February message above is a nice elaboration that helped me understand the underlying explications of the correlational wheels visuals and themes that I might not have grasped otherwise.

  7. bhenderson says:

    Hi Ed, I really like the design and visual side of your digital ethnograhy, I can see that you must have a background in graphic design! I am just wondering why you decided to produce the artifact in this way? Do you think a visually stimulating report allows the viewer to understand the content easier rather than a traditional report look?


  8. Martyn says:

    Ed, amazing visuals, reading through the further explanation above it’s really clear what you set out to do. I also think the way you reported the discussions has protected the participants and is a very ethical approach. It’s something that I definitely had issues with when trying to report my findings on the learning community.

  9. sbayne says:

    This is really interesting Ed, and thanks for the further explanation. One issue you raise for me is whether it’s possible to really analyse and critique the algorithms driving textisbeautiful without knowing much more about how they work – I think it’s fascinating the way you foreground their description of all this complexity as “our technology”!

    There’s a great paper by Introna and Hayes which interrogates the algorithms driving the plagiarism prevention ‘service’ Turnitin, which raises exactly this point. They argue that once we understand how that algorithm operates, we can then understand how Turnitin works to define and label particular kinds of students as ‘plagiarists’ – well worth a read. The additional issue they raise is that many of these algorithms are not available to analyse because they are proprietary and therefore not visible.

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