Cookies and Countries

The core subject that I have been researching in the past few weeks is ‘online cookies’. In this week’s visualisation, I am also showing various cookies that I have received in the past few weeks. I was trying to analyse where my data goes. Is it really just North America (Prinsloo, 2020)? The circle represents countries in which websites that I provided my data through cookies are based. Countries are represented by colours in their national flags to make it easier for a reader to distinguish countries.

The circle represents 12 occurrences of ‘cookies’ notifications on various websites.


  1. Most of my data go to Lithuanian companies. It probably makes sense because I am Lithuanian, and often I read the news in my native language.
  2. Only 25% of my data goes to Lithuanian companies, 75% goes to other countries. Is it fair to build profit for companies from other countries?
  3. Less than 17% of my data goes directly to North American companies. Therefore, I would not agree with Prinsloo’s (2020) claim that the vast majority of data goes to North American companies.

The Lithuanian Educational Ministry can use this data by thinking of ways that data would be available for Lithuanian companies and collaborating with them (Anagnostopoulos et al., 2013). This way, more informed educational decisions in Lithuania could be made. This would empower to make data producers themselves able to use their data, rather than only tech giants (Beer, 2019, cited by Prinsloo, 2020).

In the past few weeks, data visualisation practice helped me realise how many responsibilities an individual collects, analyses, and visualises data. The process involves various factors, and it needs a high responsibility because future decisions based on data are made. Therefore, the wrong implementation of the process can lead to various detrimental effects. Also, individuals involved in data-related processes are often pressured to simplify provided data (Williamson, 2017) and make it user friendly (Anagnostopoulos et al., 2013). This can even increase the negative effects.

Data and accountability are closely related (Williamson, 2017). It shifts control of education to policymakers and bureaucrats over educators (Fontaine, 2016). Higher data collection results in higher accountability (Anagnostopoulos et al., 2013). Educational institutions need to show themselves in an intimate account. This is what I have done in Week 10 of my data visualisations were presented the interactiveness of my lessons. It can often discourage educational institutions from collecting data and demotivate teachers who can feel surveillance.

Data shift governance from qualitative to quantitative (Williamson, 2017). It enables fast policy creation. I had experienced it myself when in Week 10, I needed to represent qualitative data quantitatively. How can quantitatively be represented experiential learning (John Locke, cited by Fontaine, 2016) or learning pictured in Platonic epistemology? These days educational systems highly rely on various testing systems. However, even testing systems themselves encourage not solely rely on tests (Fair Test, 2007, cited by Fontaine, 2016). Various schools highly focus their curriculum on what is necessary for tests (Anagnostopoulos et al., 2013), even though these tests often do not represent the whole range of skills students need to develop.

Data can help to govern. However, it needs to be considered with slight scepticism and remembrance of its non-neutrality (Fontaine, 2016). Policymakers also need to evaluate various risks when they highly rely on data (Anagnostopoulos et al., 2013).


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.

Fontaine, C. (2016). The Myth of Accountability: How Data (Mis)Use is Reinforcing the Problems of Public Education. Data and Society Working Paper 08.08.2016.

Ozga, J. (2016). Trust in numbers? Digital Education Governance and the inspection process. European educational research journal EERJ, 15(1), pp.69–81.

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. (2017). Digital education governance: Political analytics, performativity and accountability. In Big Data in Education: The digital future of learning, policy and practice. 55 City Road: SAGE Publications Ltd, p. 65.

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