End-of-Block3 Reflections

For my visualizations in this block, I decided to track my efficiency, punctuality and one aspect of well-being – data that are easily quantified, collected and defined, and which, potentially, can inform policy and governing in education. Students’ test scores, teachers’ interventions and learners’ course reviews are some more examples of indicators that have become important drivers for policy development. These ‘proxies’ of learning and teaching reflect what Ozga (2016) calls ‘thin descriptions’, stripped of contextual complexity’ (p.71) which facilitate and accelerate decision-making and enable comparison of educators, pupils and institutions across the globe.

The purposes of increased data use for governance are multiple and seem quite reasonable. ‘Data mining and big data supposedly enhance efficiency, increase transparency, enable greater competiveness, and evaluate the performance of schools and teachers’ (Ozga, 2016, p.70). It may sound like a dream come true to parents who can now participate in the life of schools and get ‘a trip advisor view’ (from Ozga, p.74) of educational institutions ‘from their sofa’. Sticking to the same goals, policy-makers can rationalize some predetermined courses of actions and shift responsibility to the ‘standards and metrics that appear as outside politics’ (Anagnostopoulos, 2013, p.11). Novice teachers may find data-intense environments stimulating and supportive.

However, the miraculous power of data, its objectivity in particular, is highly contentious. It becomes obvious if you look into a complex information infrastructure of accountability that has emerged around data use in education. According to Anagnostopoulos, ‘complex assemblages of technology, people, and policies’ (p.2) constitute the infrastructure of accountability. The author emphasizes two important things. First off, the obscurity of the system, the complex infrastructure remains largely unseen, like ‘water systems that run beneath our streets and into our homes’ (p.3). Secondly, the number of state, private, human and non-human stakeholders that participate in creating datasets required for governing with data is huge. Interestingly, some of them, like an algorithm developer or a private foundation, may have very limited understanding of education. Nevertheless, all parts of the complex infrastructure affect decision-making to this or that extent. Hence, when drawing on performance data to punish or reward teachers, it is essential to keep in mind that they are ‘a product of myriad decisions’ (Anagnostopoulos, p.2).

Although using data for policy creation is not addressing the problem of improved decisions bias wise, it has definitely contributed to creating more agile, networked and fast policies (Williamson, 2017). The time for the feedback loop between data collection and policy modification has decreased, which creates a feeling of automatic governance that some parties are probably aiming at in the future. Moreover, big data in education enables to locate low performers and best practices quite quickly and take action.

Having party addressed some of the issues, digital governance has also generated new problems in education. Gaming and performativity are the side effects of management with data. Prioritizing particular subjects, teaching to the test and pushing low performers out of school are the manipulations that some schools tap into to improve their rating (Anagnostopoulos, 2013). As a result, data-infused policies and the culture of accountability tend to exacerbate the problems of inequality, trust and customization while purporting to address them.

In conclusion, big data transforms not only the processes of policy creation, but also teaching practices and how we conceive of quality 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.

Anagnostopoulos, D., Rutledge, S.A. & Jacobsen, R. 2013. Conclusion: The Infrastructure of Accountability: Tensions, Implications and Concluding Thoughts. In, Anagnostopoulos, D., Rutledge, S.A. & Jacobsen, R. (Eds) The Infrastructure of Accountability: Data use and the transformation of American education.

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

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.

2 thoughts on “End-of-Block3 Reflections

  1. I can see that you have engaged really well with the Anagnostopoulos et al text from the course reading. You are exactly right to highlight the significance of the creation and functioning of the underlying infrastructure for how education systems, institutions, teachers and students are governed, managed and controlled–with effects (such as gaming the system) that are often unanticipated and perverse but equally consequential to the intended effects (such as holding schools accountable for student grades as a condition of state funding). You might want to consider focusing on educational data infrastructures for your assignment, if you haven’t chosen a topic already. There are multiple examples you could drawn on — learning management systems are digital infrastructures underpinning many schools and universities worldwide; Google for Education is almost a global infrastructure for digital, data-extractive education; the OECD PISA tests are based on a vast infrastructure for measuring and comparing national education systems…

    I’ve really enjoyed your visualizations and reflections throughout this course, Iryna, and I hope you feel in a confident position to begin preparing for the final assignment.

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