Block: ‘Teaching’ with data / Week 8
This week I tallied the number of times I noticed my tinnitus. Over a decade, I’ve experienced a permanent high-pitched ringing in one ear as a symptom of hearing loss. Attempting to quantify the number of times I “notice” this sound, and rank how stressful this feels, might be a useful way to communicate my subjective experience to others. But without linking this data to action it could be a harmful practice. Most tinnitus self-treatments are intended to help a person un-notice the sound through masking, relaxation techniques and exercise.
During this activity, I noticed my tinnitus more than normal. I thought I would find this more stressful as the week went on, but it didn’t. Perhaps this is because I’ve had a long time to learn how to treat tinnitus.
With this activity, I attempted to explore the affective and effective dimensions of data as suggested by Harrison et al (2020). They argue, in the context of education:
“Data are effective because their production, collection, analysis and dissemination shape modes of participation, and narrow the possibilities for teaching and learning and thus the options for action (Jarke and Breiter 2019). Data are affective, as they potentially remake both teacher (Lewis and Holloway 2018) and learner (Bradbury and Roberts-Holmes 2018) subjectivities as calculable and performative, with their capacity to render themselves knowable increasingly defined by data.” (Harrison et al., 2020, p. 402)
The data I collected is potentially effective because it sets options for treatment and action, like communicating my symptoms to a doctor or initiating self-treatment actions. In this respect, it may be of some instrumental use. But the data is potentially affective because it reshapes how I experience tinnitus – turning what is usually an experience of constancy, or even an ebb and flow, into an experience punctuated by points in time. This datafication of experience, performative as it is, is a potentially profound shift in my perception of my symptoms.
When using data for teaching, we need to consider not only that the data we’re using may be partial or based on harmful assumptions. We need to consider the rippling affects and effects of the ways we use data on our students and ourselves. This requires teachers to develop, as Raffaghelli and Stewart argue, a critical understanding of data within broader epistemological frameworks that an instrumental or technical focus can allow (2020). This is something I’ll explore further in my wrap up post for this block.
Harrison, M.J., Davies, C., Bell, H., Goodley, C., Fox, S & Downing, B. 2020. (Un)teaching the ‘datafied student subject’: perspectives from an education-based masters in an English university, Teaching in Higher Education, 25(4), 401-417.
Raffaghelli, J.E. & Stewart, B. 2020. Centering complexity in ‘educators’ data literacy’ to support future practices in faculty development: a systematic review of the literature, Teaching in Higher Education, 25(4), 435-455.