From Posts

Reflections and notes during the week. Note this category includes post with images but excludes weekly summaries.

Icons for algorithms

What icon symbolises an algorithm? Unlike cyborgs, algorithms are difficult to illustrate or visualise because they are defined not by what they are, but what they do, and by how they do those while away from the public view.

The poster below is an attempt to find a visual equivalent and a mnemonic aid for these hidden influences as enumerated by Gillespie (2014).


Critical look on the tweet archive

This post is a reaction to the visualisations of the course activity on Twitter, as created through tools such as Tweet Archivist and Keyhole, and a critical look at how the tools represent the course activity.

I think the visualizations are helpful because they take advantage of people’s ability to process visual images and because they can uncover patterns in the tweets, a considerable benefit given the volume of data and the tedium of processing it (Burchard, 2004).

That said, what first struck me was how the visualizations were presented without any introduction, as if they were self-explanatory, like photographs that stand alone by themselves. This relates to what Borer and Lawn (2013) said about how numbers and data have been instituted  to make claims about objective ways of seeing reality. The centrality of the numbers and the visualizations contrasts with how images are used, for example, in journalism where text accompanies or works together with images.

Putting the data visualizations at center stage hides the social negotiations, the subjective processes them behind this purportedly objective view. (Gillespie, 2012). Also, given the different types of data that can be captured (note that Tweet Archivist and Keyhole display different data points) and the different ways they are represented, the lack of text — about how the data was captured and represented and what they might mean — becomes a dangerous and misleading omission.

For example, the two tools show different results for top hashtags and top users, even if dates are accounted for. Furthermore, they seem to try to calculate impact through proprietary algorithms, both unexpalined, called Influencer Index (Tweet Archivist) and Klout (Keyhole) but show different results. The difference then is somewhere hidden in the algorithms.

Extrapolating from Borer and Lawn (2013), the visualizations not only make claims about objectivity, they also legitimise specific perspectives over others. Relating this to learning, the data visualizations could be misleading because they could influence how learning is defined: is learning a matter of frequency of participation, volume of resources shared, number of people reached? In other words, is learning quantifiable?

I certainly do not think so because the visualizations, at their current state, do not account for context and meaning. The question in my mind however is this all a matter of technology? If in the future, the visualisation tools cannot only count but also semantically understand the Twitter discussions, would their value be different? Would a data interpretation by a human be different from that of a machine?


Borer, V. L., & Lawn, M. (2013). Governing Education Systems by Shaping Data: from the past to the present, from national to international perspectives. European Educational Research Journal12(1), 48-52.

Burkhard, R. A. (2004). Learning from architects: the difference between knowledge visualization and information visualization. In Information Visualisation, 2004. IV 2004. Proceedings. Eighth International Conference on(pp. 519-524). IEEE.

Gillespie, T. 2012. The Relevance of Algorithms. forthcoming, in Media Technologies, ed. Tarleton Gillespie, Pablo Boczkowski, and Kirsten Foot. Cambridge, MA: MIT Press.


Possible topic: What’s the matter with data-enhanced learning?

Quite suddenly tonight, an idea for a possible assignment topic struck my head. Choosing the final topic has been simmering in my head the past week but only only now did it bubble up — so I thought I’d write this idea down, as a first attempt at least for thinking about the final assignment for the course.

The title will be: What’s the matter with data-enhanced learning? The title is a rift of Sian’s critique of TEL. The plan is to apply the three main critiques Sian raised in her article to some of the claims made about learning analytics.

The paper will try to argue how the process of data-gathering needs to be questioned; values behind the application of learning analytics, clarified; and assumptions about learning, uncovered.

I am uncertain about two things: Are the terms learning analytics and data-enhanced learning synonymous? Is the use of term data-enhanced learning justifiable for the title, though perhaps not in the body of the paper.

For references, I will rely mainly on Sian’s article for the framework of the critique of TEL. For the other part, I will cite the main claims made about learning analytics as mentioned by Ben Williamson in his presentation.

For the multimodal part, I will try to draw a sketch note that outlines the main arguments, similar to the one I had created earlier. I have seen several videos of animated data visualisations but I do not have the skills to pull of something similar. Although I will try to minimise the amount of text in the sketch note, having the flexibility of the sketch note format comes in handy. The need to minimise the amount of text in the drawing suggests that the tone may need to be somewhat punchy, or manifesto-like. Time may not be enough to pull something like that for the assignment but I am writing it down here anyway to keep it in the radar of my brain. The sketch note cannot stand alone and still relies on text to flesh out the argument. I’m unsure whether I will combine the images and text into a web page or PDF document.

A song called Android’s lament, which I tweeted in the early weeks of the course, seems appropriate for the topic. It’s opening lyric is “I will not be pushed, filed, stamped, debriefed or numbered”, which sounds like a rally against the de-personalization that happens as a by product of the processes around big data. I do not know yet how to incorporate it into the multimodal format, but the tone of its argument reminds me of Haraway’s strong and clear voice, and of her imaginative approach.

These are just preliminary ideas, some of which may not make it as part of my final assignment. However, I am interested in finding out which ideas I keep or discard in the end, and why: Creating a multimodal artefact is its own learning.

Playing with algorithms

Where can you find the world’s best pizza? What is the most popular curry recipe, headache relief? The answer, according to Google, is it depends. It depends on where you are.

This video shows how the Google search algorithm tracks geolocation and changes keyword suggestions, search results and displayed ads accordingly. Using a VPN software, I pretended that I am from several different countries (Germany, United Kingdom, Japan, United Arab Emirates and Switzerland)  The same search phrases yield different search results depending on location.

While the algorithm’s influence on recommendations about restaurants and recipes are relatively harmless, it’s influence on medical information seems like its heading to grey territory. Results from some countries are preceded by ads, pushing the actual information downwards. In fact it is quite ironic that the ads for headache relief are followed by search results for homemade remedies.

On the two types of MOOCs

Although set apart by a few years, these two videos to me seem typical of how the main proponents of xMOOCs and cMOOCs frame their accounts of the history of Massive Open Online Courses.

It is quite telling how the two types do not seem to acknowledge each other. On one hand, Dave Cormier in his account of MOOCs does not mention that the popularity of MOOCs is often attributed to platforms like Coursera, EdX and Udacity, and not the cMOOC variety. On the other hand, Daphne Koller does not acknowledge the pedagogical innovations, both early and continuing, conducted by cMOOCs. While xMOOCs have gained a mass audience, cMOOCs are often attended by educators.

Why is it that these networks have never crossed or combined? I wonder if it would be possible to combine the reach of cMOOCs with the innovation of xMOOCs. If anyone knows of a video with both cMOOCs and xMOOCs proponents has been posted online, please let me know!

Data scraping morning


This image is an initial text visualization of the posts in the MOOC I am studying. I have just spent the morning data scraping, copying text from the posts and pasting them into a spreadsheet. There are still a handful more of posts I have not yet copied, and it’s a tedious process, but it did give me a feel for the discussions in the specific forum area.

Generated with, the visualisation shows words that appear frequently together. They are grouped by color. I would like to explore the changing communication patterns so I will probably compare a few visualisations based on date. The web site also has two other options for displaying the visualisation, so I will need to go back to the readings to figure out an analytical frame for the data.

Below is a tentative cover page for the artefact.


Self-disclosure in an online community

  • What do people choose to disclose and not disclose in an online community?
  • What are the factors that influence self-disclosure in an online community?
  • How do these factors affect the sense of community?

These are some of the question I hope to explore as part of the ethnography assignment for Block 2. I will be looking an edX MOOC on transforming businesses. One of my initial impressions is that the course, because of its topic, necessarily requires a lot of self-reflection in order to define participant’s personal vision of change and leadership. However, what interests me is not the reflections themselves, but the disclosure and sharing of those personal feelings, and how that sharing is part of the community’s ethos.

According to Kozinet (2010), participation in a community is defined by the personal significance that participants attach to the activities of the community and the relationships that they build within it. My goal for the ethnography is to try to unravel these two factors, activities and relationships, in order to understand how this particular online community sustains itself.

Production notes
I will most likely incorporate word clouds as a primary element of the digital artefact. The word cloud will hopefully give a sense of the kinds of discussions that take place in the community, while allowing anonymity. Reading the discussion boards, I was struck by the emotional intensity reported by some participants. That intensity is probably be best captured as quotes, but I am wary of taking those remarks that were raised in an enclosed space and putting them in a public space. I’m still figuring out if there are alternative way of balancing representation and anonymity.

Kozinets, R. V. (2010) Chapter 2 ‘Understanding Culture Online’, Netnography: doing ethnographic research online. London: Sage. pp. 21-40.

The superphone

Thanks to Jeremy’s comment on my artefact, I looked for journal articles that make links between posthumanism and education. One of the readings came across mentions a view that assigns agency not just to humans, but also to things, hence “thing power”, or “the material and symbolic power of the non-human” (Quinn, J., 2013, p749)

I found this interesting because it draws attention to the materiality of the computers we rely on when we pursue online learning. This leads to a view of education that accounts for how non-human agents influence the learning process, and that acknowledges how those objects are crucial to human learning.

The irony is that the link to posthumanism is made through the transhumanist Superman logo.

Quinn, J. (2013) Theorising learning and nature: post-human possibilities and problems, Gender and Education, 25:6, 738-753, DOI: 10.1080/09540253.2013.831811

Image sources:
iPhone 4 vector:
Superman logo:×1080.htm