Month: December 2022

Categories
Conclusion Week 12

Final Reflections

Introduction

At the start of the the module we discussed the very fundamental question of what are ‘data’? I have to confess that this at first seemed very trivial, but as we explored this through the first tutorial and subsequent Moodle discussion, it became apparent that there was some important questions to address. Initially I thought of data as having both a raw form and an analysed form. However, it became evident to me that the term raw data is somewhat of an oxymoron (Gitelman and Jackson, 2013), as it’s never truly ‘raw’.

Williamson (2017) explains how all data is inherently partial, selective and representative. This is something seemed obvious when considering smaller-scale data like surveys and focus groups due to logistics, but something that I found more alarming when considering large digital data sets where decisions over what is recorded and how is baked into products. It became apparent that taking the time at the start of the module to consider the semantics of ‘raw data’ was an important exercise. I can certainly now relate to why Kitchen (2014, as cited in Williamson, 2017) would suggest that a more accurate word to refer to data is ‘capta’, highlighting that it’s taken, not given.

Learning with Data

In this first block, we focussed on student ‘learning’, and how this is increasingly shaped by data-driven technologies and practices. The main area of focus was on the discourses around personalised learning. A topic Eynon (2015) stresses is in need of a critical approach by researchers. Much of the affordances and pitfalls around personalised learning claims was summarised in the paper by Yi-Shan, Perrotta & Gasevic (2019) that discussed the tensions between enhancing students’ control of their learning and, at the same time, compromising their autonomy.

For my visualisation, I reflected on my own experiences of how I studied over the three week block. I was able to highlight the aspects of learning that can’t be easily recorded through learning analytics platforms, such as activities that happen offline, or in personal apps that do not feed into to any school of university performance dashboard. This activity made me reflect on how the promises of data driven learning practices are at best premature and at worst fraught with danger, despite the bullish attempts at datafication by technology providers. I also discovered that the measures reported by such systems are not accurate representation of learning, but instead only proxies at best. However, as Bulger (2016: 16) states, ‘this gap is not made explicit’.

Teaching with Data

In this block the literature and subsequent discussions focussed on the increased use of data in education and its potential effects on teaching. With large scale platformization of education well underway, this giving more power to data over human judgment and is limiting teacher autonomy (Van Dijk, Poell & de Waal, 2018). Raffaghelli and Stewart (2020) highlight how teachers are also being trained do more with data, rather than be critical of it.

My visualisation for this block focussed on push notifications over a 24-hour period, highlighting the dangers of an uncritical approach to data. Illustrating the vast amount of personalised data sent to me over one day, I was overwhelmed by what I received. Imagining this through the lens of education, it became evident that there would always be more data than time to interpret it, let alone critique it. Conducting this visual exercise made me realise that teachers have a real challenge ahead in maintaining autonomy over pedagogies and personal values as these don’t easily reduce down to data. It seems that the ‘good teacher’ is increasingly being defined as one that is familiar with their data and responsive to it. Harrison et al., (2020: 405).

Governing with Data

The final block on governing with data focussed predominantly on how institutions and educational practices are shaped by the growing datafication of educational governance. Key themes included the concept of ‘accountability’ as a way for schools, universities, and teachers to evidence effectiveness through large data sets. Aligned with this was discussions of ‘performativity’, that sees a change in practices to maximise the opportunities for performance measures.

For my visualisation I used illustrated how my devices are connected by data through different levels of integration. And technology choice is getting more difficult as I invest more and more data into ecosystems. I used this as a proxy to argue how when scaled up to educational institutions, these choices can have more profound effects, as data’s strategic and logistic importance gives rise to ‘Informative Power’ that can influence big educational decisions (Anagnostopoulos, Rutledge,& Jacobsen, R, 2013: 11).

Settling on the idea for this final block’s visualisation was particularly difficult, as I felt most of the literature referenced primary and tertiary education e.g. school inspection processed (Ozga, 2016), test based accountability (Anagnostopoulos, Rutledge,& Jacobsen, R, 2013), or data mis(use) in public education (Fontaine, 2016). As someone who has spent most of my career in Higher Education, I found it difficult to relate to some of the ideas in the same way I had with the two previous blocks. Through my visualisation I wanted to highlight issues as I saw them and for me the most logical way was to start with a simple example that could be understood on a small scale, so it could be used to help understand the issues on an institutional, regional, national and supranational scale. My visualisation therefore ended up more of a network map, rather than the result of accumulative data collection as in previous blocks.

Closing Thoughts

I have thoroughly enjoyed this reflective exercise of combining hand drawn visualisations with blog entries. The creative process of hand drawing data inspired by ‘Dear Data’ (Lupi and Posavec’s, 2016) allowed me to express ideas that would have been very difficult through text alone.

References

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.

Bulger, M. 2016. Personalized Learning: The Conversations We’re Not Having. Data & Society working paper. Available: https://datasociety.net/pubs/ecl/PersonalizedLearning_primer_2016.pdf

Eynon, R. (2015) The quantified self for learning: critical questions for education, Learning, Media and Technology, 40:4, 407-411, DOI: 10.1080/17439884.2015.1100797

Gitelman, L. and Jackson, V. (2013) “‘raw data’ is an oxymoron edited by Lisa Gitelman. The MIT Press, Cambridge, MA, U.S.A., 2013. 208 pp., illus. paper. ISBN: 9780262518284,” Leonardo, 47(3), pp. 303–304. Available at: https://doi.org/10.1162/leon_r_00792.

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

Lupi, G. and Posavec, S. (2016) Data postcards, Dear Data. Available at: http://www.dear-data.com/all (Accessed: November 4, 2022).

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

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

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, 554-567, DOI: 10.1080/02602938.2019.1676396

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

Williamson, B. 2017. Digital Education Governance: political analytics, performativity and accountability. Chapter 4 in Big Data in Education: The digital future of learning, policy and practice. Sage.

Categories
Block 3: Governing with Data Week 11

Block 3 Visualisation: The Procurement of Technology and its Governing Effects for Education

Hand drawn visualisation showing the relationship between smart devices connected by data and software ecosystems.
The final visualisation showing the personal devices I typically use along with how they’re connected to each other through data.

Both in my job and in my personal life I use a lot of technology and there are several devices that are at the core of this, including a laptop, smartphone, tablet, smartwatch and headphones which I’ve acquired over several years. Initially, decisions over which items to buy were heavily influenced by metric data in the form of price, features, pros and cons, and review scores, as well as personal preference. More recently however, as devices need updating or a phone contract comes to an end, the decision over which brand to invest in is getting more complex due to the investments I’ve already made.

Depending on which device I’m using often controls where that data is being stored. This can be anything from bookmarks and saved passwords, to backups of photos and cloud hosted documents. As I’ve started to invest more into the Google ecosystem over the past few years, decisions over buying a new piece of technology started to become governed my somewhat unconscious decisions I made years prior.

Owning devices by the same manufacturer typically gives me enhanced functionality and ease of use through a seamless integration as they’re part of the same technological ecosystem. While the choice of ecosystem I invest in for personal use largely only affects me, this is a very different when you consider this scaled up to education. When schools and universities procure technology, the implications for its users can have far more profound effects.

Within education, decisions over which technologies to invest in are not solely based upon institutions’ own requirements, as it would be for an individual using personal devices. Instead, this is often heavily influenced by technology providers through the essentialist view that assumes the technology being procured embodies their own pedagogical principles (Hamilton and Friesen, 2013). This places BigTech companies and EdTech providers in a very powerful position within education; a sector that has previously been resistant to attempts at datafication. However in recent years, perhaps with the Covid-19 pandemic serving as a catalyst, there appears to have been a step-change in the volume of data being shared with technology providers.

With VLEs and Student Information Systems capturing data on a large scale, this is giving rise to what Anagnostopoulos (2013: 11) refers to as ‘Informative Power’. In this scenario, the more schools and universities invest their data, the more this data is relied upon to make strategic decisions. This has led to the decision-making in education being decentralised from governments, schools and universities, to complex networks of data in what (Ozga et al, as cited in Williamson, 2017) as the ‘governance turn’. This in itself may not seem immediately concerning, but the software which collects this data is often designed without direct involvement with education.

Technology providers typically decide how their platforms should be used within education, without direct involvement from schools and universities. This may be in part due to education’s democratic approach to decision making, that is likely to be seen as incredibly slow and inefficient to technology companies that are used to working at speed and seeing all problems as solvable through algorithms. With technology providers increasingly working with governments and viewing themselves as thought leaders for schools and universities we’re also starting to see the development ‘fast-policies’ that are less informed by evidence-based practices and more by ‘what works’ or ‘best practice’ (Williamson, 2017: 68).

While there is now an abundance of technology available for education, the level of control given to educators has arguably been compromised. Software is now typically licensed on a SaaS (Software as a Service) basis, where the technology companies provide the software, the servers that store the data, and regular updates that institutions have very little (if any) control over. This results in schools, universities, and teachers having to change their practices to justify their worth through performance measures in what Williamson (2017: 75) refers to as ‘performativity’. Coupled with this is the concept of ‘accountability’, where the evidence of this apparent effectiveness can also be evidenced through large data sets.

With a focus on performance metrics at seemingly every level from student, to teacher, to school or university, EdTech platforms are positioning themselves as being able to help define both the problem and also provide the solution. The modern day ‘centres of calculation’ arguably happening now in schools, made possible through the software tools that are provided to education to facilitate the large scale harvesting of personal data (Latour, 1986, as cited in Williamson, 2017).

Of course not all educational institutions buy into off-the-shelf digital dashboard solutions. It’s common for wealthier institutions to resource in-house teams who can scrutinise how their data is being used and then build data-layers and reports from the ground-up based upon their needs. This is however typical of the more privileged western institutions such as Russell Group and Ivy League universities. On the other side of the spectrum, there underfunded schools who largely buy all-in-one solutions from the likes of Google and Microsoft. At the extreme end of the scale are ‘Global South’ institutions which is a term coined by Toshkov (2018) to describe anything from the poor and less-developed to oppressed and powerless.

Global South institutions as argued by Prinsloo (2020) don’t have the political infrastructures or financial resources in place to challenge datafication. Due to their oppressed situation, which has in no means been helped by their Global North counterparts, South African politicians and policymakers to are highly susceptible to the west’s ‘data gaze’ (Beer 2019, as cited in Prinsloo, 2020) with its promise of technology that is speedy, accessible, revealing, and gives a 360 degree view.

In this final visualisation I’ve attempted to illustrate how technology providers are deliberately designing solutions that rely on the large scale use of data. Whether it’s a personal device such as a smartphone, or a VLE, the premise is the same – to provide a very low and frictionless point of entry, but through accelerated datafication, it’s very impractical to get out of. Individuals and educational institutions alike are increasing their reliance on data, and rather worryingly local pedagogical practices that have previously been a hurdle are being abandoned to accommodate the functionality embedded in technology and representable through data.

Bibliography

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.

Hamilton, E. and Friesen, N., 2013. “Online Education: A Science And Technology Studies Perspective / Éducation En Ligne: Perspective Des Études En Science Et Technologie” By Edward C. Hamilton.

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. Digital Education Governance: political analytics, performativity and accountability. Chapter 4 in Big Data in Education: The digital future of learning, policy and practice. Sage.