Elizabeth's Data Visualisation Blog

A site for Critical Data and Education (an MSCDE course)


So, am I data literate now? A reflection on this blog

Through this blog I’ve become more aware of the data I produce and how data is presented to me. Hand drawing and viewing my peers’ visualisations each week made me question: how data literate am I? How often do I assume that data is telling the truth? 

I didn’t always tell the truth in my visualisations. There are reductions, conflations, and estimates in all of my work. I was interested in the affective nature of data, rather than providing truthful insights into an activity each week. My visualisations eschew accuracy and instead point to the anxieties I hold about an activity or experience. This un-truthfulness was augmented by the public nature of this blog – as much a public performance as it is a learning activity.

Given that data are social products (Williamson 2017), presenting data as truthful or objective is often a rhetorical act, as most data-driven practices try to change our perception, or to spur a course of action, through the measurement of something. With data-driven practices extending throughout education, part of a critical data literacy includes paying attention to acts of truth-telling rhetoric – especially when we are knowingly or unknowingly perpetuating that rhetoric ourselves as education workers.

Having control over my own visualisations allowed me to experiment with data methodologies. With no computer to help me collect or synthesise data, the choices I made were nominally my own – choosing what to measure, how to classify, how to draw, what to compare and what to leave out. I was sometimes frustrated by my lack of technical knowledge of statistical methods and data analysis. A comprehension of data methodologies is a key aspect of critical data literacy for education workers, but is challenging to learn given time and technical skill required, and the complexities of datafication in practice.

So, am I data literate now? I’ve gained a better understanding of how data operates in educational settings. How affective have these activities been? Below I synthesise some of the ideas that most interested me and discuss how my visualisations helped me explore these ideas in affective ways.


I was interested initially in the use of bodily data in learning analytics. For Derrida and Haraway, technologies must be understood as being inextricably entwined with the body (Barla 2018). As a turn towards bodily and emotional data in edtech occurs (Knox et al. 2020), the relationship between data, the body and learning is made explicit. There are rhetorical parallels between the promises of data-based personalised learning (Bulger 2016) and personalised health products like FitBits (Roberts at al. 2016), and material parallels as researchers use wearables and sensors to test the use of physiological data for learning analytics (Chang et al. 2018, Giannakos et al. 2020). 

This work underscores how datafication increasingly extends towards and extracts from individual bodies in education. Datafication produces student and teacher subjectivities – we come to know and perform our learning encounters with reference to the data we produce (Harrison et al. 2020). Moreover, our data proxies are metricised and ranked against other students, teachers, institutions and countries (Anagnostopoulos et al. 2013, Williamson 2017). Our ideas about learning, teaching and governing are being shaped through our individual, sometimes bodily, encounters with data.

By centring the body in this assemblage, I start to comprehend how processes of datafication exist as something real, not just an abstract concept.  I’m still thinking about my posture, my stretches, and my tinnitus, weeks after thinking ‘I can stop counting this now’. The affective power of data is so clear to me that it is literally ringing in my ears.


To me, agency in data-driven education means having an autonomous choice and voice over your data. Approaches like personalised learning, the use of data dashboards, or the development of critical data literacies all seek to empower individuals over their data in some way.

However, these approaches can’t be assumed to provide agency or empowerment to students and teachers. Individual agency in data-driven education is mediated through the asymmetrical power structures that exist in educational settings (Tsai et al. 2020). These structures are intensified through the expansion of datafication throughout education – and the cavalcade of tech industry voices that come with it. Industry actors have their own ideas about learning and teaching that they attempt to enact through the technologies they design and sell, which often tend toward a behaviourism that work against student agency and participation in their learning (Knox et al. 2020).

In these activities, I had agency over my data, but some of the visualisations reminded me of how little choice I have to engage with certain platforms at work (see Three and Four). Conversely, I have more agency than students who can’t easily opt out of having their activity datafied and may face serious consequences based on how an algorithm or teacher reads their online activity. Identifying the power and agency I actually have has made me think about how I use that power in my work.


Finally, I was interested in data as a concept, and the ‘data imaginary’ as enabler of datafication in education. Data is a powerful concept with a long history (Williamson 2017) which is specifically colonial and neoliberal (Prinsloo 2020).  Data imaginaries – visions of the future where data can solve the problems of the present – serve to expand and intensify datafication (Beer 2019) and are enacted materially through data-driven educational spaces and policies. Data imaginaries may exist in tension or align with educational imaginaries, themselves widely held visions of solutions to educational problems (Friesen 2020). 

I agree there is a dominant social imaginary that exists in education, but I prefer the plural to indicate that there are multitudes of imaginaries, existing interdependently and in resistance to the neoliberal ‘data imaginary’ emerging in critical data studies. My final visualisation attempted to explore alternative imaginaries but these remain undertheorised in educational literature (Selwyn 2020) and in this blog. Alternative critical data imaginaries exist – made visible through the efforts of data workers, in movements towards Indigenous data sovereignty (Kukutai & Taylor 2016) and Data for Black Lives (Watson-Daniels 2021), and theoretical approaches such as a queer futurity of data (Zeffiro 2019). It’s in alternative imaginaries that we might find space to assert our agency and resist the pervasive datafication –  and encroaching neoliberal social imaginary – of our educational lives.


Anagnostopoulos, D., Rutledge, S.A. & Jacobsen, R. 2013, ‘Introduction: Mapping the Information Infrastructure of Accountability’. In Anagnostopoulos, D., Rutledge, S.A. & Jacobsen, R. (Eds.) The Infrastructure of Accountability: Data use and the transformation of American education. Harvard Education Press.

Barla J. 2018, Technology/Technicity/Techne, viewed 31 March 2021, <https://newmaterialism.eu/almanac/t/technology-technicity-techne.html>.

Beer, D. 2018, The data gaze: Capitalism, power and perception. Sage.

Bulger, M. 2016, ‘Personalized learning: The conversations we’re not having’, Data and Society, 22(1), pp. 1-29.

Chang, C., Zhang, C., Chen, L. and Liu, Y. 2018, ‘An ensemble model using face and body tracking for engagement detection.’ In Proceedings of the 20th ACM International Conference on Multimodal Interaction (pp. 616-622).

Friesen, N. 2019, ‘The technological imaginary in education, or: Myth and enlightenment in ‘Personalised Learning.’ In M. Stocchetti (Ed.), The digital age and its discontents. University of Helsinki Press, pp. 141-149.

Giannakos, M.N., Sharma, K., Papavlasopoulou, S., Pappas, I.O. and Kostakos, V. 2020, ‘Fitbit for learning: Towards capturing the learning experience using wearable sensing’, International Journal of Human-Computer Studies, 136, p.102384.

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.

Knox, J., Williamson, B. & Bayne, S. 2019, ‘Machine behaviourism: Future visions of “learnification” and “datafication” across humans and digital technologies‘, Learning, Media and Technology, 45(1), pp. 1-15.

Kukutai, T. & Taylor, J. 2016. ‘Data sovereignty for Indigenous peoples: current practice and future needs’. In Kukutai , T. and Taylor, J. (eds.) Indigenous data sovereignty: Toward an agenda. ANU Press.

Prinsloo, P. 2020, ‘Data frontiers and frontiers of power in (higher) education: a view of/from the Global South’, Teaching in Higher Education, 25(4), pp.366-383.

Roberts, L.D., Howell, J.A., Seaman, K. & Gibson, D.C., 2016, ‘Student attitudes toward learning analytics in higher education: “The fitbit version of the learning world”’, Frontiers in Psychology, 7, p.1959.

Selwyn, N., 2020, ‘Re-imagining ‘Learning Analytics’… a case for starting again?’, The Internet and Higher Education, 46, p.100745.

Tsai, Y-S., Perrotta, C. & Gašević, D. 2020, ‘Empowering learners with personalised learning approaches? Agency, equity and transparency in the context of learning analytics’, Assessment & Evaluation in Higher Education, 45(4), pp. 554-567, DOI: 10.1080/02602938.2019.1676396

Watson-Daniels, J. 2021, Introducing #NoMoreDataWeapons, 26 February, https://blog.d4bl.org/introducing-nomoredataweapons/

Williamson, B. 2017, Big Data in Education: The digital future of learning, policy and practice. Sage.

Zeffiro, A., 2019. ‘Towards a queer futurity of data’, Journal of Cultural Analytics, 1(1), DOI: 10.22148/16.038

On ‘Governing’

Block: ‘Governing’ with Data / Summary

Approaches to governing and policymaking in education are increasingly reliant on data-based processes, infrastructures and regimes (Williamson 2017). Data-driven “instruments of comparative analysis” claim to identify insights (Bates 2016, p.4), to increase transparency, and to help “in sorting things out” (Ozga 2016, p.78). These claims are targeted at and promoted by an increasingly diffused network of government, non-government and corporate “experts” involved in the making of educational policy. The concurrent turns towards data and “fast policy” (Williamson 2017) work in a symbiotic way in the education sector, each working to expand the other’s reach, fueled by a specifically colonial ‘data imaginary’ (Prinsloo 2020) and large-scale processes of standardisation, quantification and classification (Anagnostopoulos et al. 2013). 

Sometimes the relationship between policy and data has the ring of a salesman with his sales kit; showing up on your doorstep, identifying a problem you have and offering to sell you the solution. “All for the low low cost of…!” (or, talking about data, “for the very transparent and ethical collection of…!”)

This is not to say that data and policy can’t be used to solve problems or improve educational environments and outcomes. For example, clear school policies that protect against homophobia/transphobia contribute to a safer environment for LGBTQ+ students (Jones & Hillier 2012) and the collection of demographic data on sexual orientation and gender identity can assist in the development of such policies (Crowhurst & Emslie 2014). Yet the requirement to provide ‘evidence’ of our lives through data to receive recognition from a cis/heteronormative policy regime is problematic for queer and trans people (Guyan 2021). The benefits and harms of data use in governing education are not as clear cut as the sales pitch would make it seem.

My visualisations this block were initially interested in the problem-solving that data and policy promise. Could data help us calculate the distributed carbon costs of online education? Could data help us predict when a student requires mental health support? The solutions I considered require the collection of data from individuals to be used as evidence for governance. This reflects a shift in interest from the accounting of an institution’s datasets to an “intimate analytics” of the individual (Williamson 2017). Individual performance data is offered up for tracking and comparison against algorithmically determined norms, shifting power from local teachers and students to national and global networks of policymakers (Fontaine 2016). If what counts is what can be counted, then individual students and teachers have to offer themselves up to be counted in order to be ‘seen’ and have their needs addressed by governing bodies and infrastructures.

In my final visualisation, I tried to visualise something that didn’t claim to solve any problems. I turned to live performance rather than hand-drawing (breaking the rules of the blog!) to play with ways of ‘capturing’ data that eschew the processes of measurement that drive the shift of “informatic power” and agency from individuals to infrastructures (Anangopoulos et al. 2013). Still, in order to show you the performance existed, I had to record it. Thus I found myself in a familiar position – having to render experience visible through digital data in order for someone else to see it. 


Anagnostopoulos, D., Rutledge, S.A. & Jacobsen, R. 2013. Introduction: Mapping the Information Infrastructure of Accountability. In, Anagnostopoulos, D., Rutledge, S.A. & Jacobsen, R. (Eds.) The Infrastructure of Accountability: Data use and the transformation of American education. Harvard Education Press.

Bates, A., 2016. Transforming Education: Meanings, myths and complexity. Routledge.

Crowhurst, M. and Emslie, M. 2014. Counting queers on campus: Collecting data on queerly identifying students. Journal of LGBT Youth, 11(3), pp.276-288.

Fontaine, C. 2016. The Myth of Accountability: How Data (Mis)Use is Reinforcing the Problems of Public Education, Data and Society Working Paper 08.08.2016.

Guyan, K. 2021. Will more data change the lives of LGBTQ people in the UK?, 11 February, http://blog.ukdataservice.ac.uk/will-more-data-change-the-lives-of-lgbtq-people-in-the-uk/ 

Jones, T.M. and Hillier, L. 2012. Sexuality education school policy for Australian GLBTIQ students. Sex Education, 12(4), pp.437-454.

Ozga, J. 2016. Trust in numbers? Digital Education Governance and the inspection process. European Educational Research Journal, 15(1) pp.69-81.

Prinsloo, P. 2020. Data frontiers and frontiers of power in (higher) education: a view of/from the Global South. Teaching in Higher Education, 25(4) pp.366-383.

Williamson, B. 2017. Big Data in Education: The digital future of learning, policy and practice. Sage.


Block: ‘Governing’ with data / Week 11

This week, I counted the number of times the word ‘data’ appears in my blog posts. Excluding references, I used this word 96 times in the posts prior to this one. In the video below, 96 drops of water are dropped into a glass to represent this word count (admittedly a very simple piece of data to work with this week). 

Prinsloo (2020) describes a ‘data imaginary’ in higher education that provides and legitimises a particular vision of what we can do with data. This imaginary also serves to expand and intensify the datafication of education, by projecting promises and fears through which neoliberal data practices can reach further into our lives (Beer 2019). How might we disentangle ourselves from this imaginary in order to imagine alternative visions of data?

Beer posits six features of a specific ‘data imaginary’: one that is speedy, accessible, revealing, panoramic, prophetic and smart (2019). By identifying features of current data imaginaries, we might clarify features of alternative imaginaries – that are un-speedy, or un-smart, for example – and the socio-political positions at play in these. I am thinking of Zeffiro’s identification of a present ‘reproductive data futurism’ in order to imagine a ‘queer futurity’ of data (2019), or how Data for Black Lives identifies methods of data weaponisation against Black people to then imagine what actions are required to create a world with #NoMoreDataWeapons (Watson-Daniels, 2021).

Throughout these weekly exercises, I’ve grown tired of the act of measuring (I don’t know how Lupi and Posavec kept this up for a year). I identified three data features I had grown tired of recording: 

  • Time (plotting data across linear time)
  • Activity (rendering actions, thoughts and the body as data points, situated in linear time)
  • Usefulness (What other time or activity can I compare this data to? Or, what can I do with this data?)

The water performance is an attempt to imagine deliberately un-useful ways of recording time and activity. The 96 data points – water droplets – exist only for the amount of time they take to fall from the dropper to the glass. The time in which the data droplets exist doesn’t relate to the time in which the original actions took place (the typing of the word ‘data’). The resulting water remains measurable but the only material relationship between this water-as-data and the act of writing is my hand, operating the unseen keyboard and then the dropper.

(After recording this, I noticed the camera couldn’t see the droplets, only the effect when they land. I also didn’t notice, while I was focused on counting, that the performance has been augmented by the sounds of a family member calling out to a cat and packing away dishes. I am left considering the unwitting effects and affects of the ways we represent data, and of the staging of data imaginaries.)


Prinsloo, P. 2020 ‘Data frontiers and frontiers of power in (higher) education: a view of/from the Global South’, Teaching in Higher Education, 25(4), pp. 366-383.

Beer, D. 2019 The data gaze, Sage.

Zeffiro, A., 2019. ‘Towards a queer futurity of data’, Journal of Cultural Analytics, 1(1), DOI: 10.22148/16.038

Watson-Daniels, J., 2021, Introducing #NoMoreDataWeapons, 26 February, https://blog.d4bl.org/introducing-nomoredataweapons/


Block: ‘Governing’ with data / Week 10

This week, I recorded the times I got out of bed each morning and the times I started work each day. I wanted to visualise the relationship between sleeping in and starting work on time while I work from home (Figure 1).

It looks like I’m more likely to sleep in when working from home. For me, this has an affect on the temporal and spatial aspects of work. The time and space of work feels more bendy, stretchy and relative (“I’ll just push back my start time” or “I’ll just answer these emails from bed”). On Friday, when I went to campus, it was like time and space ‘snapped back’ to something more fixed or intentional.

Figure 1: Get out of bed!

Links between sleep patterns and mental health are well documented. Monitoring sleep patterns like this might be a useful indicator for when an intervention into one’s health or sleep hygiene is required.

Mental health interventions were on my mind this week as I read about the University of Edinburgh’s investigation into the support provided to Romily Ulvestad prior to her death in 2020. This led me to read about ‘early indicator’ systems universities use to indicate when a student is in need of academic or mental health support. It is no surprise, after reading about the turn towards ‘future-tense’ governance and ‘fast policy’ (Williamson 2017), that data-driven ‘early indicator’ systems are being tested to track individual students’ mental health using multiple sources of continuous data, including learning analytics (Foster 2019, Office for Students 2019). I wonder how useful learning analytics are for this – academic participation is not always correlated to signs of worsening mental health (although this appeared to be an aspect of Ulvestad’s case). I am also wary of universities accessing personal data to drive these predictive systems. This week’s data is not something I would ongoingly share as a worker or a student (although others might be comfortable doing this, if they thought the collecting body would use that data responsibly).

Overall, I think it’s a mistake to turn to predictive data-driven systems to indicate when a student is in need of mental health support. These are expensive distractions from the difficult policy and governance work that’s needed to address the issues students encounter when trying to get help from under-resourced and over-burdened student support systems. 


Foster, E 2019 ‘Can learning analytics warn us early on mental health?’, WonkHE, 18th July https://wonkhe.com/blogs/can-learning-analytics-warn-us-early-on-mental-health/

Office for Students 2019, Innovation, partnership and data can help improve student mental health in new £14m drive, viewed 20th March 2021, https://www.officeforstudents.org.uk/news-blog-and-events/press-and-media/innovation-partnership-and-data-can-help-improve-student-mental-health-in-new-14m-drive.

Weale, S & Baldwin, J 2021 ‘Edinburgh University admits failings after student kills herself’, The Guardian, 16th March, https://www.theguardian.com/education/2021/mar/16/edinburgh-university-admits-failings-after-student-kills-herself-internal-review-support-mental-health.

Williamson, B 2017 ‘Digital education governance: political analytics, performativity and accountability’, in Big data in education: The digital future of learning, policy and practice, Sage.


Block: ‘Governing’ with data / Week 9

This week I inventoried the physical objects I use for the Critical Data and Education course (MSCCDE). I wanted to answer the questions:

  • How many physical objects do I use for this course vs how many things do I need?
  • How many resources do I consume through the act of online study?

Originally I wanted to document a physicalisation of these objects instead. I was inspired by Song Dong’s Waste Not installation, and the ways in which materialising data might underline the more-than-human and more-than-digital aspects of human-data-technology assemblages (Lupton 2019). But for space-related reasons I had to stick to a 2D version of this (see Figures 1 & 2).

Then, after reading how imbricated processes of quantification, standardisation and classification transform local knowledge into metrics (Anangnostopoulos et al. 2013), I decided to apply this process to my visualisation just for fun (Context for claim of ‘fun’: I was a library cataloguer before moving to education roles).

I created a 5-minute schema based on my questions and similar specifications I’ve seen used in education e.g. ACARA, CEDS (see Figure 3). I then attempted to standardise and classify my cartoon objects in a way that could be read and analysed by a machine (see Figures 4 & 5).

Figures 1 & 2 and 4 & 5 represent the same objects, and are both mirrors into my way of learning. While the latter figures might prove more useful for answering my questions, thanks to quick classification, the former figures reveal less quantifiable ways in which I relate to and use these objects.

There is a bit of play in each set of figures, represented by the mirror in Figures 2 & 5: I don’t actually use a mirror for study, but for MSCCDE I create ‘data mirrors’ each week through these activities.

It turns out I use a lot of things, many of which I don’t really need but happen to have on hand, and many of which consume power. Next I would try to calculate how much power I actually use. Governing bodies might be interested in data like this for calculating the cost of online learning borne by students. Particularly where carbon consumption shifts away from the campus to households (Filimonau et al. 2021), this data could contribute to more accurate calculations of the carbon costs associated with online education.


Anagnostopoulos, D., Rutledge, S.A. & Jacobsen, R., 2013. Introduction: Mapping the information infrastructure of accountability. In, Anagnostopoulos, D., Rutledge, S.A. & Jacobsen, R. (Eds.) The Infrastructure of Accountability: Data use and the transformation of American education.

Filimonau, V., Archer, D., Bellamy, L., Smith, N. and Wintrip, R., 2020. The carbon footprint of a UK University during the COVID-19 lockdownScience of The Total Environment756, p.143964.

Lupton, D., 2019. Data selves: More-than-human perspectives. John Wiley .

On ‘Teaching’

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.


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. 

Figure 1: Tinnitus Tally. Times I “noticed” tinnitus, ranked on a scale of how stressful I found the “noticing” each time.

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 literatureTeaching 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.

Figure 1: Energy levels over three days

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.


Raffaghelli, J.E. & Stewart, B. 2020. Centering complexity in ‘educators’ data literacy’ to support future practices in faculty development: a systematic review of the literatureTeaching 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.

Figure 1: Messages sent via Microsoft Outlook and Teams

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.

On ‘Learning’

Block: ‘Learning’ with Data / Summary

This block, I tracked different bodily data while learning and working – my posture, movement, and reflected gaze. I wanted to experiment with the encoding of the body through data as a way to explore the turn towards bodily and emotional data in edtech (Knox et al. 2020). If the edtech imaginary treats the failings of educational systems as “engineering problems to be solved at scale” (Friesen 2019, p. 144), then the turn towards bodily data posits our bodies as part of the ‘problem’ to be ‘solved’ by edtech and learning analytics. This is an obvious and problematic endpoint of data practices based on behavioural approaches – or, at least obvious to anyone who has experienced similar exertions of control over their bodies based on their gender, disability, sex or race.

I didn’t set any clear questions to be answered in this block, choosing to explore and think about the construction and curation of bodily data instead. But my experimenting so far feels only surface level and, despite inspiration from the ‘Dear Data’ project, I struggled to track and visualise data in ways that I felt reflected the complexities of intra-action between the mind-body and data while learning or working (Rogowska-Stangret 2017). Still, I posited that a ‘knowing’ awareness of the methods of data collection and visualisation could provide a site of performative resistance to surveillance technologies and learning analytics, in a similar way to how a knowing awareness of gender performance informs drag acts.

There are some obvious problems with this position. Firstly, the increasing complexity of learning algorithms work to obscure from students the ways in which their learning activities and bodies are datafied and shaped, as does the increasing insertion of behavioural interventions and nudging across the educational landscape (Knox et al. 2020). Secondly, existing power relationships and imbalances in complex educational systems (Tsai et al. 2020) limit the ability of students to enact their agency in even performative ways. My position comes from three weeks of self tracking data, a learning activity that encouraged my own agency and power over the data I curated. There are evidently different power dynamics at play when a student is sitting a high stakes exam with mandatory online proctoring, or having to agree to terms of use in order to matriculate (Tsai et al. 2020), and this limits their ability to resist or question the ways in which their data will be collected and used.

The act of self-tracking made me think about how I ‘pay attention’ to learning activity and data production and when I’m happy to let a machine ‘pay attention’ on my behalf. Can an awareness of the rules of the game, so to speak, help develop learners’ empowerment over their data? Although increased transparency and personalisation of data (for example, through personalised data dashboards) might help to develop a ‘knowingness’ of data practices, we cannot automatically assume that this will lead to student or teacher empowerment over their learning (Bulger 2016, Tsai et al. 2020). Assuming also that teachers should explain to students how their data is being collected ignores the ways in which power dynamics in educational systems and complex data processes obscure teachers’ comprehension of data practices as well. 

It’s these tensions and complexities I’ll carry in the back of my mind as we move into our next block on ‘teaching’ with data.


Bulger, M, 2016, Personalized learning: The conversations we’re not havingData and Society22(1), pp. 1-29.

Friesen, N 2019, “The technological imaginary in education, or: Myth and enlightenment in ‘Personalised Learning.” In M. Stocchetti (Ed.), The digital age and its discontents. University of Helsinki Press, pp. 141-149.

Knox, J, Williamson, B & Bayne, S 2019, ‘Machine behaviourism: Future visions of “learnification” and “datafication” across humans and digital technologies‘, Learning, Media and Technology, 45(1), pp. 1-15.

Rogowska-Stangret, M 2017, Body, viewed 13 February 2020, <https://newmaterialism.eu/almanac/body/body.html>.

Tsai, Y-S, Perrotta, C & Gašević, D 2020, Empowering learners with personalised learning approaches? Agency, equity and transparency in the context of learning analytics, Assessment & Evaluation in Higher Education, 45(4), pp. 554-567, DOI: 10.1080/02602938.2019.1676396