End-of-block2 Reflections

In this block, I aimed to deepen my understanding of how the ubiquitous processes of datafication and personalization in education are affecting the concept of teaching and the role of a teacher. It turned out that current trends that impact teaching in the data-intense society are similar to those we came across in the discourses about learning.

First off, there is a conflict between a ‘dataist’ trust in the ‘magic’ of digital quantification’ (from Williamson,  p.352) and a reductive, subjective nature of education-related data collection and visualization. Learning analytics and educational platforms have become new ‘must-haves’ not only for commercial vendors but for public institutions as well. Big data advocates argue that digital tools can augment and facilitate pedagogical practices or, more radically, replace the traditional educator.   

To realize the limited potential of LA to enhance instruction, it is essential to look into the data they present. As a rule, they measure students’ engagement (attendance, emotions, time) or predict learners’ performance using algorithms. These metrics are highly contentious and can hardly reflect the complexity of the teaching/learning process. ‘What is learner engagement’ or ‘whose opinions are embedded in the algorithm code’ (O’Neil) are not the full list of questions that should be raised before using LA as ‘proxy measures of the performance of staff, courses, schools, and institutions as a whole (from Williamson, p.354)’.

The assessment of teacher efficiency via students’ quantified behavior risks to transform pedagogy ‘to ensure it ‘fits’ on the digital platforms that are required to generate the data demanded to assess students’ ongoing learning’(Williamson, p.358). Brown’s research on the use of LADs in the classroom (2020) demonstrates that instructors ‘expressed frustration with the ways that data displays undermined their existing pedagogical strategies’ (p.384) bringing little value in return. According to Prinsloo, ‘seldom learning analytics technologies align with pedagogical conceptions and theories, stemming mainly from developers’ priorities rather than educational processes (from Rafaghelli, p.439)’. Thinking of why teachers have little say in the process of education datafication, it is essential to remember who ‘sets the tone’ in this domain and their driving values (Van Dijck, 2018).

Faced with all the pressures of the ‘ranking’ society and numerous instances of data harm (O’Neil), educators should be looking for ways to resist the adverse effects of datafication. Academics are starting  ‘with a restatement of the inherent social and public good of higher education’ (from Williamson, p.362). Interestingly, Van Dijck (2018) describes how the core values of public education have been recently compromised by the Big Five, however, little is said about how the alarming tendencies can be resisted.

Maybe, understanding what is happing and why can be a good step ahead. More and more researchers (Sander, Raffaghelli and Steward) emphasize the importance of critical data skills development for educators to push back on technocratic control and unwelcome surveillance.

Drawing on the insights from the readings as well as my own data collection practices, I can conclude that datafication and personalization ‘as the mantras of a new educational paradigm’ (Van Dijck, p.18) are reshaping the role of teachers: ‘from classroom directors to dashboard controllers’ (Van Dijck, p.7), from ‘sages on stage’ to ‘sages engaged in data-informed divination’ (Brown, p.396). This simplified vision of the teacher functions is affecting pedagogy, sense and decision-making at all levels and can be destructive.


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

O’Neil, C. 2016. Weapons of Math Destruction. Talks at Google. Available at: https://www.youtube.com/watch?v=TQHs8SA1qpk

Raffaghelli, J.E. & Stewart, B. 2020. Centering complexity in ‘educators’ data literacy’ to support future practices in faculty development: a systematic review of the literatureTeaching in Higher Education, 25:4, 435-455, DOI: 10.1080/13562517.2019.1696301

Sander, I. 2020. What is critical big data literacy and how can it be implemented? Internet Policy Review. 9(2) DOI: 10.14763/2020.2.1479

Van Dijck, J., Poell, T., & de Waal, M. 2018. Chapter 6: Education, In The Platform Society, Oxford University Press

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

3 thoughts on “End-of-block2 Reflections

  1. This post offers an engaged and thoughtful summary of some of the key critical points from the literature related to teaching with data. I like the way you have identified from your reading how the role of the teacher is being reshaped into a different form, involving different kinds of practices. For me, one of the key issues is how data and its visualization narrow the field of perception of the teacher towards (seemingly) definite numerical or graphical outputs. Certain features of teaching and/or learning are rendered more visible and highlighted for action than others. I wonder what you would see as viable forms of “resistance”, as you’ve put it, to these developments? Is there scope for introducing “critical data literacy” to teacher development programs, whether in schools or HE?

  2. It is definitely not easy to resist current tech developments in education and avoid their ‘side effects’, especially if you work for an institution. For me, critical data literacy is more logical to be part of a master’s level program, but that means that not all teachers will be ‘enlightened’. Maybe, it is a good idea to start educating managers first so that they communicate the critical approach further down the hierarchy? Not sure they enjoy a lot of autonomy either though.

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