Governing Overview Reflections

Block 3: Summary

During the ‘governing’ block, I created visualisations on technology-enabled interactions, getting help while injured, and anxiety sparked from three different categorisations of my life – personal, work, and school. The visualisations and readings focused my thoughts on three main themes with policymaking and governance in mind:

  1. The purpose of data: What is purpose of the data and is the data ‘good’?
  2. The context/value of data: How can the context or value of the data be better included in the outcome?
  3. The power of data: Who holds the power of data?

What is purpose of the data and is the data ‘good’?

Policymaking and governance are reliant on data to provide insights, serve as evidence, and enhance transparency for the purpose of decision-making (Ozga, 2015). The ultimate goal is to know what is ‘good’ and what is ‘bad’, e.g. are the students learning, do they have the skills deemed necessary to advance, are the teachers effective, what schools are doing well, etc. Ironically, however, “Rather than empowering the people, the data may constrain what people know and how they think about their schools” (Anagnostopolous et. al., 2013). This is why getting to the answer of purpose and what is considered as ‘good’ data is important.

Note: the definition of ‘good’ for this discussion is what is useful and true. In an ideal scenario, this would also include data that does not infringe on someone’s privacy; however, certain private data points may be useful and true to the policymaking.

As a result of a ‘need to know’ culture and pressure to create policy and governance, the process appears to start with the end result rather than starting with the data and through analysis, finding an outcome regardless of ‘good’ or ‘bad’. While this is the scientific process, i.e. stating a hypothesis, making a prediction, and testing to determine the outcome, the readings give a sense that the process of iteration is limited when it comes to creating education policy. ‘Fast policy’ is the result of the increased number of actors (human and non-human) in education policymaking (Williamson, 2017). More data is being collected, enabled by the increased use of technology and improved infrastructure; however, the context seems to be forgotten as the game of telephone is played with the data after collection (Anagnostopolous et. al., 2013). The data collected travels through many actors and processes by the time it reaches those using it for policymaking and/or reporting. It is also invariably influenced by those doing the collecting (Ozga, 2015).

Williamson (2017) quotes Bowker (2008: 30), “A good citizen of the modern state is a citizen who can well be counted – along numerous dimensions, on demand.” This statement assumes all aspects of us as individuals can be quantified, yet this is not true. There are aspects of us as individuals that cannot be neatly quantified, defined, or categorised as evidenced by my own attempt to track anxiety. As a result, determining what ‘good’ data is, is complex and one that needs iteration and agility. ‘Fast policy’ and the use of technology may enable this iteration, if the assumption is that the policymakers are willing to be as agile and change existing policy as new information is available. The ideal for many would be that the data serves the education system (and it’s policymaking and governance) rather than a political or material purpose, which is often the case (Pinsloo 2020).

How can the context or value of the data be better included in the outcome?

Anagnostopolous et. al. (2013: 7) state, “Assigning students, teachers, and schools a numerical rating and classifying them as ‘failing’ or ‘effective’ says little about the causes of failure or what can or should be done about it and about school success and how to maintain it.” Context is important in understanding the data, but the context cannot always become a data point itself. For example, not all context is a quantifiable data point that can be added to, or understood by, a technology tool. Examples of this could include emotions and skills that are difficult to categorise neatly, like creativity and emotional intelligence.

In my own visulisations during this block, the context became key to understanding my own data as simply looking at the data points without knowing that I had, for example, been injured one week would dramatically change the interpretation and outcome. Imagine if the data was collected on a student, but the student was unable to provide that data point because it wasn’t possible in the system, or available as a question. The policy created from these data points, which become an indicator of performance, would likely not be ideal.

The statement made by Anagnostopolous et. al. aligns well to this: “As they define what kind of knowledge and ways of thinking matter and who counts as ‘good’ teachers, students, and schools, these performance metrics shape how we practice, value, and think about education” (2013: 11).

Who holds the power of Data?

The data that is now collected, is not only controlled only by government, but also non-governmental organisations like private sector companies (Williamson, 2017). These non-governmental organisations have increasing influence over education as they have a seat at the table to decide what can be inputted into the systems, the research that should be done, who (or what) completes the analysis of the data, and who will have access to the data.

Anagnostopolous et. al. (2013: 7) state, “Determining what kind of information about the nation’s students, teachers, and schools is collected and how it is processed, disseminated, and used, by whom, in what form, and for what purposes involve questions of power. They also reflect and privilege particular ways of thinking, particular values, and particular notions of worth.” What this highlights is that the student, the teacher, and the school that the data is collected on, no longer holds the power of their data. The power is held by the non-governmental organisations and governments who are analysing and reporting on the data. Similarly, this was a reason why I personally didn’t want to collect or highlight certain things in my own visualisations. As soon as the data has left my hands, the power to it has also left.

Taking the ‘infrastructural perspective’ approach (Anagnostopolous et. al., 2013), more time should be spent on identifying what data is collected for what purpose as well as how it is collected, and ultimately, pushed upstream to the end consumer. This large-scale datafication process involves countless actors (human and non-human), and the outcomes are now often readily available to those far beyond the school where it was collected (Williamson, 2017). Ultimately, there is a danger of a layer of abstraction as the data can become vague, or general, be interpreted from numerous perspectives, and end up being used in ways that were not originally intended (Anagnostopolous et. al., 2013). This is a key point when thinking about policymaking and governance in education. The hope, nonetheless, is that the policies and governance enacted benefit those in the education system, rather than limit or hinder them in any way.



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.

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 SouthTeaching 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.

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