Final Reflections

The data tracking and collecting process, together with designing and hand-drawing the accompanying visualisations helped me to understand the importance of the data that is collected and how it is presented. The impact this task had on me mirrored that of Giorgia Lupi and Stefanie Posavec (2015) and their discussion of ‘noticing awareness’. By manually capturing my various learning activities, I became not only aware of my actions but also its potential impact that this could have on my future actions. The act of collecting the data itself was perhaps as, if not more, impactful as the final visualisation and analysis of that data, as this ‘noticing awareness’ has extended to an awareness of the overall context and digital ecosystem within which this data is situated. It has helped highlight the decision making and design processes that underpin data and data visualisations, which ultimately inform and influence our decisions and actions as learners, teachers, and institutions. 

By being fully immersed in the process of data collection and analysis, I became acutely aware of its subjectivity, selectivity, and lack of context specificity. I was able to don the hat of one of the many actors that make up the dispersed socio-technical networks or complex assemblages of people, technologies and policies (Williamson, 2017). I was the coder, the designer, the researcher, the educator; dictating what data could and should be seen, and how that narrative should then be played out. By doing this, I began to notice the wider context within which these data streams and visualizations operate, taking a step back to question the discursive shift to align education with the broader narrative of Silicon Valley and data solutionism (Knox et al, 2020) and to understand the persuasive and rhetorical function of data, questioning its apparent objectivity and neutrality (Williamson, 2017). In this regard, the process has helped develop my own critical data literacies, moving from a tendency to focus on technical know-how and skills development to considering the wider issues of power dynamics, agency, equity, ethical considerations, and the impact of technological developments such as AI, machine learning and predictive data use. 

Moving beyond the rhetoric 

Knox et al (2020) discuss the broader narrative of Silicon Valley solutionism, alongside the dataification of society and rise of learning analytics, which frames data with a revolutionary and transformative quality that looks to radically enhance a particular sector or social practice. An assumption is made that there is not only something to fix but that data provides an avenue to ‘penetrate the fog’ and reveal learnings that were previously unimaginable. There is an overriding assumption that these technologies and data will enhance teaching practice and learning processes. That having the knowledge and data at your fingertips and utilising these digital technologies will result in automatically improved instructional practice. Data solutionism has become accepted rhetoric; technology will ‘fix’ education. 

The enduring myth and pursuit of personalised education and the dreams of dialogue (Freisen, 2020) are heavily entwined in the rhetoric of technology and education. New technology promises personalised education at mass scale: creating equal access, democratic student centred instruction, the enhancement of student agency, and the adaptation of learning to a student’s unique set of goals, interests and competencies. In block one, learning with data, I wanted to experiment with my own learning, improve my understanding of how and why I behave in relation to my learning practices, and from these insights modify my behaviour accordingly. I fell in line with this rhetoric. And on reflection, at times throughout the nine weeks, I naturally fell back into looking at my data through the lens of performativity and accountability, looking to find a solution in order to fix a problem, and treating the data as objective fact.  

Moving beyond the rhetoric, beyond the notion of data solutionism, I began to examine, understand and question how and why data are classified, standardised, categorised and presented. This highlighted how subjective and selective data collection and analysis can be, allowing me to move beyond accepting data as objective fact and questioning how these techniques of accountability and performativity shape our learning, teaching, and governing processes. 

Complex paradox 

Equally by moving beyond this rhetoric, a complex paradox is revealed; digital innovation promises emancipation, empowerment, equity and enhancement of agency while furthering a pervasive governance culture and exerting ‘algorithmic control’ over education (Tsai and Gašević, 2020). There has been an ideological shift that may redefine education as a commercial enterprise rather than for the common good, promoting a new concept of learning and advancing the interests of the service providers and programme developers (Van Dijck, 2018). It is against this backdrop, the neo-liberal imaginary, and potential ideological arm wrestle that concerns with the datafication of education are revealed. 

Throughout all three blocks, the power dynamics and tensions that exist within education are revealed through discussion about algorithmic control, metric power (Williamson et al, 2020) , and informatic power (Anagnostopoulos & Jacobsen, 2013). Knox et al (2020) depict data-driven technologies as reintroducing behaviourist theories of control that reduce student agency by designing choice architecture that nudges a learner in a particular direction through tracking, predicting behaviours, emotions and actions. Equally, categorisation, measurement, and classification of people and activities can lead to pedagogical reductionism, reducing a teacher’s autonomy, and potentially can render some learning invaluable as it cannot be dataified (Williamson et al, 2020). The final theme, governing with data, neatly tied this together by examining our historical and systematic trust in numbers and statistical knowledge. Through data streams, real-time monitoring, and predictive data use existing techniques of accountability and performativity have been redeveloped to amplify, accelerate, and expand the scope of numerical data. Through the extension of this power, the various actors and networks can extend their reach beyond policy, defining what kind of knowledge and ways of thinking matter, shaping the narrative and our future actions, educational practice, and learning activities. 

Critical data literacies

The promise of increased visibility, understanding, and empowerment of students, teachers, and institutions is something that purveys the marketing rhetoric of digital innovation and data driven technologies. The last nine weeks has highlighted the need for the development of critical data literacies so that educators can be part of the ongoing conversation and shape the future development and design of online education. The ‘rhetoric of transparency may privilege seeing over understanding’ (Tsai and Gašević, 2020, p.558) and it is integral that people understand the world around them, not only so they can take an active part in society, but so they can critique and question the rhetoric, not blindly accept data as objective fact, and shape the ongoing narrative within 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.

Friesen, N. (2020). The Technological Imaginary in Education: Myth and Enlightenment in ‘Personalized Learning’. In Stocchetti M. (Ed.), The Digital Age and Its Discontents: Critical Reflections in Education(pp. 141-160). Helsinki University Press. doi:10.2307/j.ctv16c9hdw.12

Lupi, G. & Posavec, S. (2015) Dear Data – Opening Keynote, Eyeo Festival.

Knox J., Williamson, B & Bayne, S., (2020) Machine behaviourism: future visions of ‘learnification’ and ‘datafication’ across humans and digital technologies, Learning, Media and Technology, 45:1, 31-45, DOI: 10.1080/17439884.2019.1623251

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

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.

Williamson B., Bayne S., & Shay S. (2020) The datafication of teaching in Higher Education: critical issues and perspectives, Teaching in Higher Education, 25:4, 351-365, DOI: 10.1080/13562517.2020.1748811

Van Dijck, J. (2018). Education. In The Platform Society. : Oxford University Press. Retrieved 1 Mar. 2021, from

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