Category: Teaching (page 1 of 1)

Block: ‘Teaching’ with Data / Summary

The datafication of education goes hand in hand with the commodification of education. By datafication, I mean the increased quantification of teaching and learning activities and expansion of metric power in education systems (Williamson et al. 2020). By commodification, I mean the packaging of these quantifiable activities into marketable commodities. 

Teachers are increasingly positioned as data workers who produce, interact with and monitor data about their students. They increasingly may come to see and define their students through ‘data doubles’ produced within educational technology platforms (Brown 2020, van Dijck et al. 2018). The assumptions made by technologies about their students are not always made explicit to teachers.

Teachers experience a similar data subjectification as their students, coming to know and define their practice through data (Harrison et al. 2020). Their work as teachers (and researchers) is increasingly quantified in ways that reward specific professional performances as ‘good’, serving to reinforce and reproduce the processes of datafication and commodification within education systems.

Accordingly, teachers’ data literacy and competency is put into the spotlight in this block’s readings, and critical data literacy is positioned as a necessity. Critical data literacies focus on inquiring into data systems and their use at both a structural and personal level (Sander 2020), including how data flows through commercial platforms (van Dijck et al. 2018), and use theoretical frameworks to examine the assumptions and norms about education that are reinforced by data practices (Raffaghelli & Stewart 2020). This is in opposition to instrumental conceptualisations of data literacy that focus on navigating or making use of data without connecting these to epistemological, pedagogical and ethical complexities.

I wonder if it is possible for teachers to develop this form of critical data literacy while remaining ‘illiterate’ in many of the ways in which data work. As I read this interview with Katherine McKittrick on her new book, Dear Science and Other Stories, I was struck by her description of trying to create an algorithm:

I wanted to create, on my own, an endless algorithm… When I began researching how to do this, I became very frustrated. I could not do it. I did not have the background in computer science… I remember sitting there, just sad and embarrassed. Because I had to face how insolent I was for thinking I could just “make an algorithm” and disregard the difficult work computer scientists do. I had to come to terms with the fact that my understanding of algorithms was largely descriptive and that most of what I knew was largely negative (algorithms underpin racial profiling) and that I was understudied.

(McKittrick 2021)

Although McKittrick is talking about creating an algorithm for a specific purpose, this account made me think that a conceptualisation of critical data literacy must be based in both epistemology and methodology, or the ‘work’ of data science. While avoiding a purely instrumental approach to data practices, teachers should be taught explicitly about data collection, analysis and visualisation methods. Critically data literate teachers not only need to develop critical onto-epistemological positions towards data (Harrison et al. 2020), they need an understanding of how data scientists and other data workers actually work. To me, this is an important aspect of a critical data literacy for teachers that could foster an understanding, or sense of solidarity, with data workers across industries who both produce and are subject to similar processes of datafication and commodification.

References

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

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), pp. 401-417.

McKittrick, K. 2021. ‘Public thinker: Katheirne McKittrick on Black Methodologies and other ways of being’, interviewed by Chanda Prescod-Weinstein for Public Books, 2nd January. <https://www.publicbooks.org/public-thinker-katherine-mckittrick-on-black-methodologies-and-other-ways-of-being/>.

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), pp. 435-455.

Sander, I. 2020. ‘What is critical big data literacy and how can it be implemented?‘, Internet Policy Review, 9(2). DOI: 10.14763/2020.2.1479

van Dijck, J., Poell, T., & de Waal, M. 2018. ‘Chapter 6: Education‘, in The Platform Society, Oxford University Press.

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.

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.

References

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.

Block 2: ‘Teaching’ with data / Week 7

This week, I was very tired. I drew the ebb and flow of my energy over three days. Those are the lines you see below.

The icons were added after I drew the lines. They were an attempt to reflect upon and codify the circumstances around my wavering energy levels. Activities like eating or travelling were easy to classify and see links to changes in energy level. Yet by attempting to codify what was happening, I felt like I simplified the complexities of the circumstances. Especially the attempt to codify what was happening “in head” – I immediately regretted reproducing this superficial CBT-style classification of thoughts and emotions. The assumption that you could read this data and make connections between my activities, thoughts and energy levels over three days reproduces an intrumentalised approach to human psychology that I personally only find occasionally useful.

This activity made me think about the assumptions and norms that inform the datafication of education, and the instrumentalised forms of behavioural and educational psychology that these can draw from. I agree with Raffaghelli & Stewart (2020) that approaches to ‘data literacy’ for teachers should interrogate these assumptions and norms and what they reinforce and represent. What data can ‘show’ you about your students is not just a partial or simplified view, it is a view that might reinforce harmful or misrepresentative views of your students, based on particular pedagogical and psychological assumptions.

References

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.

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.

References

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.