Overview Reflections

Final Visualisation Blog Summary

Considering data from the perspective of learning, teaching, and governance has been a useful exercise, in which I have reflected on the collection, analysis, and presentation of data in education. In the first block, I was reminded that data in isolation may not demonstrate learning, even though sometimes some data is better than no data (Brown, 2020). In the second block, it became clear that the datafication and commodification of education is changing the role of the teacher (Williamson et al, 2020). Lastly, in the third block, the power of data was apparent as the governance perspective shed light on the “questions of power” in relation to the type of data collected, how it is understood and communicated, and for what purposes (Anagnostopolous et. al., 2013: 7).

In the first block, I tracked music habits, Twitter notifications, phone usage, emails and questions. Each visualisation was bound by time, highlighting the passing of time through data in a way that became unexpectedly personal. When comparing the visualisations, I was reminded that “what counts as education when it comes to digital data is what can be counted” (Williamson, 2017: 46). For example, counting emails is simple; however, tracking emotional engagement with Twitter is complex because it can be fluid, and is not easily bound by time. The question is if either demonstrates learning, and if so, if one is preferred over the other. Working in technology, it is a good reminder that data is personal, needs context, and that not everything can be counted.

Furthermore, there should be careful consideration of the type of data collected and the purpose as certain data impacts data privacy (Bulger, 2016). As technology usage increases, data privacy concerns will become increasingly complex, because artificial intelligence and other technologies can be used to track a student’s every move. For example, facial recognition can be used to read and understand student emotions (Chan, 2021), and wearables augment the type of data points available about the human body while learning (Knox et al, 2019). In my personal data reflection in Week 4, I analysed the data collected by my iPhone. While I was pleased to see that I spent 2 hours exercising in the dashboard for my “quantified self” (Eynon, 2015), the phone only tracks the app usage time, not the fitness value without the integration of a wearable technology. From a learning perspective, this highlights that further reflection is needed to assess if the “simple act of using numbers” does indeed demonstrate learning, or simply highlights that something happened as the 2 hour block shows in my iPhone dashboard (Eynon, 2015).

In the second block, I tracked sleep, emotions, and distractions under the assumption that these impact student engagement. Behavioral data could be used for gamification and personalised, or adaptive, learning if artificial intelligence or wearable technology was integrated into learning platforms (van Dijck et al, 2018); however, these are not data tracked by learning platforms today and raise data privacy concerns. In my technology experience, engagement is tracked by mouse clicks, time, comments, etc. From a teaching perspective, this highlights the importance of selecting valuable data points because a dashboard can limit the view that a teacher has, and in turn, impact their perspective of students (Williamson et al, 2020). For example, my visualisations provide a limited view of what impacts my ability to engage. Additionally, the dashboard could unknowingly limit teaching methods rather than positively impact them (Brown, 2020). As highlighted by Bulger (2016: 4), in classrooms, teachers leverage learner-centered instruction and personlise teaching based on “interpersonal cues…. subject matter expertise… knowledge of how people learn, and knowledge of each student, to determine individual needs, adjusting their lessons in response to questions and behaviors.” In the remote classroom, this is not as easily accomplished. The teacher needs dashboards to bridge the gap both from a learning and teaching perspective.

Similarly, this limited perspective may be transferred to teachers, if using the data for performance purposes because the data can become “proxy measures of the performance of staff, courses, schools, and institutions as a whole” (Williamson et al, 2020: 354). A distinction is needed between the data collected to demonstrate learning and the data collected to demonstrate teaching methods. This is an interesting consideration when remembering that the data actors are not always educators, but technology companies and other non-governmental organisations (Williamson, 2017). From a technology perspective, more data is an easy upsell, which translates to additional revenue and happy shareholders. From a teaching perspective, more data is not always beneficial when teachers may lack necessary skills to analyse dashboards and recognise bias in an algorithm that produced the dashboard (Brown, 2020).

In the third block, I tracked technology-enabled interactions, getting help, and anxiety with a focus on the purpose, value, and power of data through the governance perspective. The physical act of collecting data and creating the visualisation demonstrated all three as it became the beginning of understanding the complex layer of abstraction that influences governance, which in turn is pushed back down to teaching and learning. As such, learning, teaching, and governance become a cycle of, and for, data. How data is collected is invariably influenced by collectors (Ozga, 2015), but even more important, is acknowledging the number of actors (human and non-human) that interact with the data before it becomes a performance metric, or a report (Anagnostopolous et al., 2013). Technology improvements and actors like the ‘Big Five tech companies’ (van Dijck et al, 2018) have enabled the datafication and commodification of education, giving rise to ‘fast policy’ and influence over the education system (Williamson, 2017). This results in “questions of power” from the initial collection through dissemination of the data (Anagnostopolous et. al., 2013: 7).

In summary, a simplistic view of data in education is that it provides an opportunity to demonstrate learning, assign a value to teaching, and serve as insight or transparency for governance (Ozga, 2015). The visualisation task enabled a view into data for the purpose of learning, teaching, and governance, highlighting that this simplistic view is far from the truth. The data process – from collection to dissemination – most importantly, highlighted the separation of the student from the data and the risk of generalisation and unintended perspectives (Anagnostopolous et. al., 2013). Lastly, it continuously reinforced that what matters is what can be counted (Williamson, 2017), and ultimately, that data impacts “how we practice, value, and think about education” because it allows for the categorization of the good and the bad (Anagnostopolous et. al, 2013: 11).

Word Count: 1008 without citations, 1087 with citations



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 dataTeaching in Higher Education. 25(4), pp. 384-400

Bulger, M. 2016. Personalized Learning: The Conversations We’re Not Having. Data & Society working paper. Available:

Chan, Milly. (2021, February 21). This AI reads children’s emotions as they learn. CNN Business. Retrieved from

Eynon, R. 2015. The quantified self for learning: critical questions for education, Learning, Media and Technology, 40:4, pp.407-411, DOI: 10.1080/17439884.2015.1100797

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, 15(1) pp.69-81

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. 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 perspectivesTeaching in Higher Education. 25(4), pp. 351-365.

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