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Block 2: Teaching with Data Introduction Week 8

Block 2 Visualisation: Teaching with Data

A visualisation that shows the push notifications sent to an individual persons smartphone over a single day.

My visualisation for Block 2 ‘Teaching with Data’ stems from Raffaghelli and Stewart’s (2020) recommendation to expand data literacy to include critical, ethical and personal dimensions as well as technical proficiency. Raffaghelli and Stewart explain that most of the focus in education so far has been on training teachers to be more data literate, and to make more use of abundance of data available to them. Taking this concept on face-value, I chose to experiment with an uncritical acceptance of digital data for 24 hours. For one day, I enabled every available push notification on my smartphone and recorded what I received. Within this visualisation, I also attempted to categorise the notifications into those that were purely information based, those that were attempts to pull me back into the app, and those that were direct targeted ads. The result was a very disruptive and frustrating day, as I frequently dismissed notifications that were largely irrelevant.

While this 24 hour experiment is a tenuous and somewhat crude analogy of how teachers are affected by datafication, it allowed me to surface contentious discussion points often raised by educators. Through experience and training, teachers continually strive to develop and improve their practice. They will often draw upon the softer skills of teaching such as humour, empathy, and personal judgement to engage students in learning. However, in a world where decisions are increasingly informed by big data sets, these autonomous teaching practices can be seen as ineffective and inefficient. As stated by (Van Dijk, Poell & de Waal, 2018), “Datafication and personalization are pushed as the mantras of a new educational paradigm where human judgment is increasingly replaced by a product of predictive analytics”.

The use of digital technology in education seems to have gone well beyond our supplementary ideals. Driven by rose-tinted views of the potential of big data, educational infrastructure has largely been moved into the cloud and teaching and learning practices are increasingly being shaped by the feature sets of the technology available. As summarised by  Harrison et al., (2020: 402) “It seems we cannot now think or experience education without thinking or experiencing data”.

So how does my visualisation relate to the teaching with data? While as an individual, I have a lot of autonomy over what data I choose to use and engage with, this isn’t quite the same in education. The way that schools operate – and in turn teachers teach – is increasingly becoming a reactive process in response to metrics such as university league tables and student feedback. This is referred to by the term ‘performativity’ by Harrison et al., (2020). Whilst not direct measures of teaching, these learner focussed metrics are then frequently used as proxies of effective teaching. So in the case of my notifications dashboard, dismissing or turning off these notifications could be seen as bad practice, or at least not using data to its potential. More desirable action would be to look at the notifications through an essentialist perspective, believing that there’s lots of potential there that I should be able to utilise if I invest more time in the ecosystem.

There is a danger that in order to respond to these metrics in a way that scales, teachers will be put under pressure by their institutions to change their pedagogies to ensure everything fits on the digital platforms that are required to generate the data demanded to assess learning. (Williamson, Bayne & Shay, 2020). It appears that the argument against teacher autonomy is not only compromised at an institutional level, but also at a national level as governments are also endorsing platformisation whilst ignoring academic autonomy (Van Dijk, Poell & de Waal, 2018).

What is needed is a more critical view of datafication, where teachers are encouraged to challenge assumptions made by predictive analytics and learning dashboards. This should include more transparency over big data practices and the risks and implications associated with it, which can in turn empower more responsible use of technology in future (Sander, 2020).

Throughout my education, some of the most influential teachers were those who had strong personal values, used humour, and at times probably used unorthodox pedagogies to engage students. However, it now seems that these personal values are being lost to as they don’t easily reduce down to data entities or scale up to repeatable practices that can be enacted through digital platforms. It now seems that the ‘good teacher’ is defined as one that is familiar with their data and responsive to it. Harrison et al., (2020: 405).

Bibliography

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, DOI: 10.1080/13562517.2019.1698541

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, DOI: 10.1080/13562517.2019.1696301

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.

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Introduction

Introductory Post: Reasons, Reflections & Hopes for CDE

As I reach the end of the introductory section of this module ‘Critical Data and Education’, it provides me with an opportune moment to reflect on my reasons for studying this course; what I hope to achieve at the end; and what particular aspects of the topic of ‘data’ interest me most. In this short post, I’ll

Reasons for studying the course

I started studying the MSc Digital Education at September 2020 and since my first course ‘IDEL’, I’ve been drawn to the topic of data. When the courses have given me the flexibility to explore this area, I have done so. In my IDEL essay I looked at ‘Who Benefits from Digital Education?‘ where I took a critical view of the role of BigTech companies and their unrelenting attempts to infiltrate education during the pandemic. I continued to explore data in other courses and most recently looked at whether teachers can play a bigger role in the development of educational technologies.

In my assignment ‘the sociomaterial dilemma’ for the course for Education, Data & Culture, I explored whether teachers can play a bigger role in the development of educational technologies.

My last module was actually Research Methods, but my reason for studying this earlier than recommended, was that I wanted to study Critical Data and Education, as this is a course that I believe will play a key role in helping me define my dissertation topic and question. Up until this point, I have looked at critical perspectives of data on a fairly macro level, largely focussing on BigTech companies. Whilst hugely relevant, these perspectives have always felt too vast for a dissertation and I honestly wouldn’t know where to start. Through studying this module I hope to sharpen my focus and identify an area of critical data and education that I can explore in my dissertation.

What I hope to achieve by the end of the course

I’ve probably alluded to this in the paragraph above, but through studying this course I hope to achieve two things. Firstly, I hope to delve deeper into the topic of critical data. In the past I’ve been drawn to the topic of critical data, but it’s sometimes been just one element of a larger topic. In this course, I can focus on critical data practices over a longer period of time and in more depth than previously afforded.

Through focussing on critical data over 12 weeks, my second hope is to come away with a clear – or at least clearer – understanding of my dissertation question, which I’m certain will be on the topic of critical data in Higher Education.

What particular aspects of the topic of ‘data’ interest me most?

Coming from a background as a Learning Technologist, I feel well-versed in speaking to people about the affordances of digital technologies. However, prior to starting the MSc in Digital Education, I realise I probably only had a fairly surface-level appreciation of how data is being used through these technologies. I guess I was focussed on the bits people see – the ‘front-end’ as developers would say.

I’m really interested in a number of different areas from the practices and malpractices of Big-Tech and EdTech companies in education, and their predominant focus on decision makers and students in favour of academic staff.

On the institutional side of things, I am also interested in the more local practices that happen within schools colleges and universities. On this topic, I’m interested in the challenges HE are facing now with resourcing people who can contribute to such discussions of datafication. Whilst we want academics, researchers and digital education professionals in these discussions, it’s not easy to resource or support this, with increasing demands such as higher student numbers. Often the focus is on expansion which results in more of a dependency on choosing off-the-shelf cloud-based technologies and solutions. In an ideal way institutions would first establish clear strategy, policy and workflows for data use first and foremost. However in practice this is not often the case and decision-makers are listening to the voices of external EdTech companies who claim increased efficiencies, scalability and automation.