Block 2: Week 8 Visualisation

How am I blogging?

This week I decided to collect data on the how of my engagement with the student blogs.


For seven days I recorded all my blog activity (Eynon’s (2015) ‘what’), and categorised it by how I engaged: visiting, looking (for items), reading, writing, and thinking. I also noted the order in which I took part in these activities and if there were any pauses in activity overall.

Results and Analysis

I decided this time to create a story around my activity, since it has a sequence of phases: posting; reading comments on my posts and responding; reading other posts and commenting etc. How this sequence plays out might be of interest to the teacher who created this whole activity.

Though I have kept time as an axis, I have manipulated the traditional straight line representation to draw attention to the fact that, sequence may be important, but the degree of association between activities may not be based on their difference in time.

I also suggested, through the visualisation, how visible these different activities would be to a teacher, to allow comparision between the available and (part of the) unavailable data.

Interaction with the blogs over a  week.
The story of my blogging.

This depiction of time is inspired by the ideas of gyrification of the cerebral cortex of the human brain, causing folding, which it is thought to help speed communication between brain cells (beyond those that are ajoining).

A linear depiction of time can suggest the further things are apart in time, the weaker/more distant the relationship, whereas this depiction suggests things (and time) are not always that simple. And time is something that is measured and reported as a common feature of learning systems reports and dashboards (suggesting it is important): a dashboard will have already decided how you view data such as time (pre-set options being only an illusion of teacher personalisation and control).

A teacher may look for the story of a student’s enagement in a dashboard, but find no more than occasional glimps of selected (by whom?) types of activity. A very large proportion of even the behavioural activity here is rendered invisible because of the methodology of data collection and visualisation (making the student look initial partially then completely disengaged).

How does this relate to teaching?

  • The dashboard answers the research question of its own choice based on the data it has elected to collect and share which, if nothing else, is based on its technical capabilities, rather than needs of staff or students.
  • Any options available to teachers when using the dashboard are an illusion of control: all decisions of importance have already been taken.
  • Data the learning system does not/cannot collect, is so undervalued as to be not worth mentioning (Williamson et al, 2020) to the teacher.
  • When a teacher looks at a dashboard (or VLE report), there is no indication (warning?) that this is partial and incomplete picture of ‘what’ happened and completely missing the ‘why’.
  • Dashboards always presents students in a superficial, datafied form, potentially affecting the teacher’s impression of them and thereby their relationship.
  • As before, if this data is visible by the institution, this may be read as a proxy for the teachers ability, e.g. to teach, engage their students, or use the learning technology effectively (Williamson et al, 2020).


Eynon, R., 2015. The quantified self for learning: critical questions for education. Learning, Media and Technology, 40 (4), pp. 407-411, DOI: 10.1080/17439884.2015.1100797

Williamson, B. Bayne, S. Shay, S. 2020. The datafication of teaching in Higher Education: critical issues and perspectives. Teaching in Higher Education. 25(4), pp. 351-365.

2 Replies to “Block 2: Week 8 Visualisation”

  1. This is very inventive, and quite powerful in illustrating the (in)visibilities of data software used in education. Time, as you’ve astutely observed, is often a significant aspect of data collection. Duration of an activity may be taken as an indicator of engagement; temporal measures of the interval between logins or activity may be taken as signals of ‘risk’ and so on. It’s worth questioning whether these are really ‘educational’ measures, or just artefacts of the affordances of the software, as you have identified here. A longer history of this kind of software might explore its origins in ‘business intelligence’ software in industry too, and techniques of employee performance measurement and productivity management used to ensure competitive advantage. Do you think this is just a simple matter of translating software from one context to another, or does something more than the software itself translate across from the business to the education sector? Does such tech also, perhaps, bring with it certain cultural and economic assumptions? For industry, of course, finding business efficiencies is a prime concern, which may help explain the centrality of time as a measure in business intelligence systems. Your reflections above raise some really rich possibilities for further exploration.

    1. Thank you for your feedback.

      I think we are used to measuring things in terms of time; it’s often easy to do and we are brought up with the idea that time is ‘precious’ and valuable (‘time is money’). Getting things done quickly is admired and rewarded; ‘slow’ is often a criticism or even an insult.

      It’s easy to translate the ideas of time & motion studies performed in a factory to a school setting, without thinking about whether it is appropriate (if you had started at first principles, with no existing models, you would have arrived at the same end point?)

      Even if it does appear to offer useful insights, the software, protocols etc, will all be imbued with the values used to create them, which would bias the outcomes towards the priorities they were designed for, not necessarily those of education.

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