How am I engaged?
This week I decided to collect data on the how of my engagement in all my work and study projects.
For five days I recorded all my activity (Eynon’s (2015) ‘what’), and categorised it by how I engaged: watching, listening, reading, considering, speaking and writing. I also noted if I was working alone or in a group.
Results and Analysis
I decided this time to focus on creating an easy-to-read visualisation; it might be thought that to a teacher, I’m just part of a large cohort, so there wouldn’t be much time to view my individual progress.
Since I was analysing activity, rather than using time, I simplified the data by grouping it together by the ‘project’ (one line of the visualisation for each), then reduced its representation to the minimum possible (a single letter or symbol). This reduction was to enable quick visual analysis.
Furthermore, to enable a quick valuation of the data, I deliberately used a representation to suggest a judgement of how I was engaged in each project.
This is inspired by the ideas of negative space and that what is not there can get more attention that what is: what are your eyes drawn to in the following image and what assumptions are behind that?
If a dashboard shows you what is not present, does that imply it is ‘missing’ and that it should be there? Would you have looked for it, if it had not been indicated? What is missing and not acknowledged?
Learning activities are already loaded with value (e.g. passive or active) by the individual. What if a dashboard suggests some data are more valuable than others, but doesn’t make its underlying bias as obvious as my visualisation; how much could be accepted without question?
How does this relate to teaching?
- It might be suggested that a dashboard makes a complex situation easy to understand. However, such a property is commonly accepted as only positive whereas, in this case, simplification is at a cost.
- What a teacher sees on a dashboard, will be determined by what the learning system has already been programmed to record, and what of that is shared. The teacher may be in a position to select from that but may still have to accept how it is displayed, which in some way transforms it. There is, at best, an illusion of teacher empowerment, but that may be no more than choice over pre-selected options.
- There will, of course, be data that cannot be recorded, and any data that is self-reported may be valued less highly (Williamson et al, 2020). That a dashboard differentiates between the value (trustworthiness?) of data in a display, could be suggestive in itself. Again, this may be outside the teacher’s control.
- If this data is visible by the institution, this may impact how a teacher views it: a proxy for their teaching, rather than about student activity (Williamson et al, 2020) and a means whereby they are datafied (Williamson, 2016). This may impact their teaching as, if they feel it reflects on them, they may make changes in order to ‘improve’ the dashboard, rather than for pedagogic reasons (Brown, 2020). They may also experience this as surveillance, disempowering and diminishing any autonomy they have.
- Dataist comparisons between teachers (Williamson et al, 2020) may not make sense, but may be enabled by the learning system itself. That a dashboard is made so simple, is applied across all contexts, with data collected and centralised, could be said to invite comparison. So, as strategies for students are identified through their datafication, teachers might find themselves nudged by predictive analytics.
Brown, M., 2020. Seeing students at scale: how faculty in large lecture courses act upon learning analytics dashboard data. Teaching in Higher Education. 25(4), pp. 384-400.
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. 2016. Digital Education Governance: Data Visualization, Predictive Analytics, and ‘Real-Time’ Policy Instruments. Journal of Education Policy. 31 (2), pp. 123–141. doi:10.1080/02680939.2015.1035758
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.