Final blog entry

This course helped me to think about data in education from different sides. I understood the relationships between data & learning, data & teaching and data & governing.

Data & Learning. Key findings:

  1. Most time outside, I spend on Thursdays and Saturdays. A limitation has occurred because the intensity of activities was not measured. This can be seen as a black box in the analysis (Tsai et al., 2020).
  2. The intensity of physical activities does not have a clear impact on my willingness to study. However, the collected data was only about me, and it is not neutral. Data collector imprint needs to be considered (Knox et al., 2019).
  3. Around 25% of the time during the week, I spend learning.

Data related to personal activities can help think about learning (Eynon, 2015, p. 407). This can encourage students and facilitators to have conversations together (Stocchetti, 2020) and provide useful feedback (Ifenthaler & Erlandson, 2016, p.1). However, not all data can be used when thinking about learning. Therefore, what factors are taken need to be carefully assessed.

It is important to consider how learning can be represented quantitatively? How can motivation or enjoyment be represented in numbers? Learning is a complex subject that can be challenging to do it. Black-boxes (Tsai et al., 2020) can not be left because they can also result in various consequences for individuals. For instance, it can shift learners attention from learning to data (Wise et al., 2013, cited by Eynon, 2015).

On the other hand, data can provide various improvements in the learning process too. It can have motivation, meta-cognition, informed learning choices and other support (Eynon, 2015).

Data & Teaching. Key findings:

  1. Time spent preparing for lessons does not have a clear effect on performance. This type of personal data can help teachers to evaluate their performance and make necessary conclusions.
  2. On average, I receive 2 cookie requests a day. Most common cookies are related to data used for security, personalized ads and performance.
  3. The website recommended by teachers to students can impose various student privacy related threats.

Data can show ‘truths’ about teaching (Beer 2019, cited by Williamson et al. 2020). However, data needs to be collected by teachers themselves because otherwise, it can also be considered surveillance (Brown, 2020). Often learning analytics can negatively intervene in the learning process. Tools need to be aligned based on practitioners values and views. Collecting data myself about my preparation time helped me to feel more comfortable and reveal realistic results. If my workplace tracked me, I would have consciously or even unconsciously would spend more time on lesson preparation.

Data literacy is an underrated skill crucial in today’s world (Knox et al., 2019). This can help teachers to find unknown unknowns. It is essential that an individual would have in mind various societal, individual and institutional contexts. However, some can argue that critical education understanding and technical skills can hardly fit together. I have not managed to find much analysis in the existing literature. Therefore, further research is necessary.

Cookies can be useful for various educational purposes. It is widely used in the online education platform ‘Coursera’. It focuses on various datafication and personalisation principles  (van Dijck et al., 2018). According to my findings, ‘Coursera’ collects more student data than any other online platform I have encountered. Data should be used ethically, foundations of privacy and data security should be followed (van Dijck et al. 2018), which can often result in less profitable decisions. It is essential that data provided by students would be analysed by educators who concentrate on learning rather than profit. Teachers would consider the analysis and various societal, institutional and individual issues (Raffaghelli & Stewart, 2020).

Data & Governing. Key findings:

  1. 66% of my lessons are interactive. However, I am not sure if my educational institution is satisfied with this result. Data that I collected was personal to me, and I presented myself in an intimate account (Anagnostopoulos et al., 2013). I used various colours to represent the logos of platforms to make easier associations to readers.
  2. 75% of my personal data go overseas. It contradicts the fact that most personal data go to North American companies (Prinsloo, 2020). In data visualisation, different colours were used to represent flags of various countries.

Governance can often influence datafication by encouraging students to use various platforms that could collect data. It becomes more probabilistic due to the quantitative approach that is used when analysing data. Many educators think that data does not need a high degree of interpretation and can speak for itself (Anderson, 2008 cited by Ozga, 2016). This can help to implement ‘fast policies’ (Williamson, 2017). Big data means massive participation in policymaking. The quantitative approach often did not show various details in my visualisations, and various black-boxes were left (Tsai et al., 2020).

Datafication in governance is often used to increase performativity. Governance often sees data as a solution to various problems (Ozga, 2016). However, it also increases accountability (Williamson, 2017). A higher degree of data collected in institutions means that more data needs to be revealed, and they need to present themselves in an ‘intimate account’ (Anagnostopoulos et al., 2013). It can often discourage educational institutions and teachers from datafication. I would not be very enthusiastic about my educational institution collecting personal data about my educational decisions.

Educational governments can collaborate more together and come up with conclusions (Anagnostopoulos et al., 2013). This can help to make more informed educational decisions. For instance, the Lithuanian government can collaborate more with other governments or tech-giants and make better decisions.

Visualisations design

I often used inspirations from ‘Dear Data’. However, I tried to combine other ideas and my own. I tried to use many different colours and shapes to make it interactive and appealing to the eye. For instance, I have drawn actual cookies to represent online cookies. Also, I tried to use particular lines to symbolise the intensity of things that I tried to visualise. I made visualisations simple and user friendly (Anagnostopoulos et al., 2013) that a reader can understand right away what is shown. However, this often encouraged simplicity (Williamson, 2017) resulted in many black boxes (Tsai et al., 2020).


Data visualisations helped me to learn a lot of facts about myself and my behaviour. Collecting, analysing and visualising data helped me to see datafication process as a whole. Overall, data has many advantages and disadvantages and its non-neutrality need to be remembered (Fontaine, 2016). Therefore, critical thinking and evaluation of data and visualisations are crucial.


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.

Brown, M. (2020). Seeing students at scale: How faculty in large lecture courses act upon learning analytics dashboard data. Teaching in Higher Education, 25(4), 384-400.

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

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.

Ifenthaler, D. & Erlandson, B.E. 2016. Learning with Data: Visualization to Support Teaching, Learning, and Assessment, Technology, Knowledge and Learning, vol. 21, no. 1, pp. 1-3.

Knox, J., Williamson, B. & Bayne, S. (2019). Machine behaviourism: future visions of ‘learnification’ and ‘datafication’ across humans and digital technologies, Learning, Media and Technology, 45(1), pp. 1-15.

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.

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), pp.435–455.

Stocchetti, M. (Ed.). (2020). The Digital Age and Its Discontents: Critical Reflections in Education. Helsinki University Press. doi:10.2307/j.ctv16c9hdw

Tsai, Y., Perrotta, C., & Gašević, D.. (2020). Empowering learners with personalised learning approaches? Agency, equity and transparency in the context of learning analytics. Assessment and Evaluation in Higher Education, 45(4), 554-567.

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. In Big Data in Education: The digital future of learning, policy and practice. 55 City Road: SAGE Publications Ltd, p. 65.

Leave a Reply

Your email address will not be published. Required fields are marked *