End-of-Block1 Reflections

Collecting data about my own learning and creating hand-drawn visualizations turned out to be a beneficial exercise. It demonstrated that I was only able to capture behaviour-related signals that were easy to measure, as time spent on the task, tools/people involved, number/kinds of messages, etc. However, those ‘indicators of learning’ make only the tip of the iceberg, whilst complex cognitive, social and emotional processes that are part-and-parcel of knowledge construction remain unnoticed.

Although hand-drawn data visualization seems to have less constraints and bias than learning analytics presented by conventional digital tools, it became obvious that any data representation is subjective, partial and devoid of context. No matter how much data you captured, there will always be more. In other words, any data is always a reduction of complex reality when we intentionally leave out something that we consider less important.

However, when students are responsible for data-related choices (collecting, visualizing, interpreting), like in our case, there are two apparent benefits. First, in this setting, learning analytics can become truly empowering and has the potential to enhance students’ agency, which, as Tsai et al (2020) describe in their research, is not always the case with data-intensive technology in the world of learning. Secondly, managing one’s own data withdraws the acute problem of ‘dataveillance’ from the educational agenda that was also described in the above-mentioned article.

To conceptualize learning from the data-driven perspective, it was essential to realize that the hype around learning analytics is connected to the utopian imagery of personalized learning able ‘to fix the outmoded management and practices of educational institutions at various levels’ (Friesen p.142). Interestingly, this vision has lived in the educational discourses for centuries. Dreaming of ‘Aristotle for every Alexander’ (Suppes), ’2-sigma benefit’ (Bloom) and ‘following one’s own bent’ (Asimov), widely exploited by tech companies, few stakeholders tend to question the benefits of personalized learning. However, as Bulger (2016) reveals, there is very little research into ‘what personalized learning systems actually offer and whether they improve the learning experiences and outcomes for students’ (p.3).

Conceiving of learning in the algorithmic age, it’s essential to keep in mind that learning is not only about living beings. In the work devoted to machine behaviourism (2020), the authors stress that machine learning systems ‘appear to work against notions of student autonomy and participation, seeking to intervene in educational conduct and shaping learner behaviour towards predefined aims’ (p.32). Since AI technologies are becoming more common in education, they transform the role of a learner as the central figure and the consumer of educational services described by Biesta (2013), and turn them into, as Knox et al mention, ‘prized products, from which valuable behaviours can be extracted and consumed by ever-improving algorithmic systems (p.35)’.

Overall, the readings and my attempts to quantify learning in this block have deepened my understanding of how data-intense technologies have been modifying the concept of learning. Despite the fact that the big promises of personalized learning are inspired by old-age ideas, they can never be realized to the full, since education-related questions are part of a bigger agenda concerned with power distribution, success and sense of life.    


Biesta, Gert. 2013a. “Giving Teaching Back to Education: Responding to the Disappearance of the Teacher.” Phenomenology & Practice 6 (2): 35–49.

Bulger, M. 2016. Personalized Learning: The Conversations We’re Not Having. Data & Society working paper. Available: https://datasociety.net/pubs/ecl/PersonalizedLearning_primer_2016.pdf

Friesen, N. 2019. “The technological imaginary in education, or: Myth and enlightenment in ‘Personalised Learning.” In M. Stocchetti (Ed.), The digital age and its discontents. University of Helsinki Press.

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.

Tsai, Y-S. Perrotta, C. & Gašević, D. 2020. Empowering learners with personalised learning approaches? Agency, equity and transparency in the context of learning analytics, Assessment & Evaluation in Higher Education, 45:4, 554-567, DOI: 10.1080/02602938.2019.1676396

2 thoughts on “End-of-Block1 Reflections

  1. A very throughtful and engaged commentary on the development of your own thinking over recent weeks. I very much applauded your comment that “‘indicators of learning’ make only the tip of the iceberg, whilst complex cognitive, social and emotional processes that are part-and-parcel of knowledge construction remain unnoticed”. It’s essential to remember that educational data are always only indicators, signals or proxies, and necessarily limited in their explanatory power. It’s great to see you engaging effectively with the set course reading to help develop and substantiate your own reflections too. I’m looking forward to your further reflections on the ways that data affect teaching too.

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