Block: ‘Teaching’ with data / Week 6
This block, I’m interested in the ways teachers and educational workers “see” students through data. Data and metrics don’t necessarily reduce our “view” of students and their activity, but they do affect how we might limit, compare, treat or change that view (Williamson et al. 2020). Learning analytics dashboards and other data-driven technologies can provide teachers more insight into a student’s activity than they would otherwise have, especially in very large classes, and may provide an indicator of when a teacher needs to “intervene” somehow. Yet this view is produced and constructed by multiple human and non-human parties. These parties include the makers of learning analytics software, the algorithms behind that software, teaching staff and students themselves.
This week, I continued to self track data around my work. I recorded every time I sent a message through Microsoft Outlook or Teams. I tried to replicate a work-based version of the basic “participation” data that an LMS/VLE might provide a teacher on a student.
This visualisation is a reduced view of my work, which can be helpful – I gained an insight into how long I worked this week and when I worked through my breaks. To someone else this is a view without context. How you interpret this visualisation is subjective, your best guess at what a flurry of messages at 4pm on Wednesday meant.
Of course, all of this activity is happening through Microsoft. So I know from my daily Cortana emails that an algorithm is also – with access to more data than is recorded here – taking its best guess at what “commitments and follow ups” I made on Wednesday.
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.