Block 1 Reflections – Learning With Data

Over the past three weeks, I explored various ways of data visualisation inspired by the Dear Data project (Lupi and Posavec, 2018). For me, this involving evaluating quantitative measurables and qualitative data in a meaningful way. Other than my own interest in mindfulness and the growing research of its use in education (Maynard et al., 2017), I chose to focus on mindfulness for the qualitative data it would provide. The examples in the Dear Data project are that of story-telling and learning of another’s life experience as opposed to quantitative data analysis for improved learning; the focus of the data visualisations produced, therefore, was less about statistical analysis and more about recording observations related to the block theme.

Eynon (2015) suggests having a solely quantitative approach to evaluating learning, can lead to datafication of learning by shifting the focus of attention away from learning to the quantitative measures itself. This essentially is a difference between qualitative themes such as ‘quality of writing, mathematical thinking or cognitive process’ to qualitative areas such as multiple-choice questions answered, words written, time spent revising. School performance in the UK being measured on progress-8[1], puts a focus on ‘the measures of learning’ as opposed to the learning itself, often leading to a culture based on rewards and punishments (Kohn, 2000). What is evident regarding measures of learning vs actual quality of learning, is that the data is a representation, often simplified or reduced, of the actual reality it is representing, much like the thought of an event isn’t the event itself yet our nervous system doesn’t differentiate between the two (Kabat-Zinn, 2011). I found this phenomenon in my visualisations too, as data captured, both qualitative and quantitative represented only certain elements of what was being observed and measured, and much of which had to be discarded for simplicity and meaningful understanding. As a learner in the process, the daily data lead to small corrective changes and offered a more meaningful analysis than the weekly overview.

Mindful learning suggests a more intrinsic definition of the term based on the original Latin educere – to bring forth (what is within), as compared to the conventional use of learning used in ‘learnification’ being based around the idea of ‘vessels being filled’ and hence commodified (Langer, 2016). Such intrinsic learning values creativity and discovering one’s element i.e. of systematically identifying one’s talents and passions and then allowing for the circumstances for it to develop (Robinson, 2011). One can argue such an approach allows for far more meaningful personalisation of learning and which isn’t necessarily technology centred based on ‘adaptive and algorithmic understanding of learning activity (Thompson and Cook, 2017) but a ‘long-established tent of good teaching’ (Bulger, 2016). Education technology now seems to be offering plausible solutions of personalisation and to the holy-grail of education: Blooms-2-sigma problem (Friesen, 2020) in which mastery-based learning[2] produces 2-standard deviations better results than conventional classroom teaching of 30 students (Bloom, 1984). While there is increasing ‘datafication’ of education and growing use of learning analytics’ and ‘emotional learning analytics’, (Knox, Williamson and Bayne, 2020), such data is limited to binary digits and therefore even an AI machine-like Alpha-Go, as sophisticated as it may be, is a system limited to quantitative measures and therefore incomplete[3], and so wouldn’t be able to define and learn about a qualitative process such as mindfulness, unless imaginary numbers, asymptotes and undefined values begin to carry meaning. An area which I would be keen to explore in light of post-humanism and the observer-observed non-duality at the quantum level is the learning potential of quantum computing and human consciousness as an assemblage of distributed cognition (Yibin, 2019).

 [1] Progress 8 is a measure of the progress children make between the end of primary school and the end of secondary school. See

[2]  ‘feedback with corrective procedures and parallel formative tests’ with a good tutor produced 2-standard deviations better results than conventional classroom teaching of 30 students and a standard deviation better if mastery learning was used in a class size of 30 (Bloom, 1984).

[3] See Godel’s Incompleteness Theorem: No consistent formal system that contains a certain amount of finitary number theory can prove its own consistency


Bloom, B. S. (1984) ‘The 2 Sigma Problem: The Search for Methods of Group Instruction as Effective as One-to-One Tutoring’, Educational Researcher, 13(6), pp. 4–16. doi: 10.2307/1175554.

Bulger, M. (2016) ‘Personalized Learning: The Conversations We’re Not Having’, p. 29.

Friesen, N. (2020) ‘The Technological Imaginary in Education:: Myth and Enlightenment in “Personalized Learning”’, in Stocchetti, M. (ed.) The Digital Age and Its Discontents. Helsinki University Press (Critical Reflections in Education), pp. 141–160. doi: 10.2307/j.ctv16c9hdw.12.

Frontiers | Mindfulness-based interventions in schools—a systematic review and meta-analysis | Psychology (no date). Available at: (Accessed: 2 February 2021).

Kabat-Zinn, J. (2011) ‘Some reflections on the origins of MBSR, skillful means, and the trouble with maps’, Contemporary Buddhism, 12(1), pp. 281–306. doi: 10.1080/14639947.2011.564844.

Knox, J., Williamson, B. and Bayne, S. (2020) ‘Machine behaviourism: future visions of “learnification” and “datafication” across humans and digital technologies’, Learning, Media and Technology, 45(1), pp. 31–45. doi: 10.1080/17439884.2019.1623251.

Kohn, A. (2000) ‘BURNT AT THE HIGH STAKES’, Journal of Teacher Education, 51(4), pp. 315–315.

Langer, E. J. (2016) The Power of Mindful Learning. Da Capo Lifelong Books.

Lupi, G. and Posavec, S. (2018) Observe, Collect, Draw! Journal. 1st edition. Princeton Architectural Press.

Maynard, B. R. et al. (2017) ‘Mindfulness-based interventions for improving cognition, academic achievement, behavior, and socioemotional functioning of primary and secondary school students’, Campbell Systematic Reviews, 13(1), pp. 1–144. doi:

Robinson, K. (2011) Out of our minds: learning to be creative. Fully rev. and updated edition. Hoboken N.J.: Capstone.

Thompson, G. and Cook, I. (2017) ‘The logic of data-sense: thinking through Learning Personalisation’, Discourse: Studies in the Cultural Politics of Education, 38(5), pp. 740–754. doi: 10.1080/01596306.2016.1148833.


Yibin, X. (2019) ‘Research on the interaction between quantum entanglement and thinking consciousness’, Cluster Computing, 22(3), pp. 6599–6607. doi: 10.1007/s10586-018-2354-1.

2 Replies to “Block 1 Reflections – Learning With Data”

  1. This is a very good summary of the first block Saqib. You are drawing on relevant course literature, and reflecting on some interesting links between ‘mindfulness’ and learning.

    ‘was less about statistical analysis and more about recording observations related to the block theme.’

    It is worth pointing out the differences in subject matter that the Dear Data project explores, however it would also be good to reflect more on the similarities between this work and more established data visualisation. The Dear Data visualisations would still count as descriptive statistics, just not collected and represented in the ways we tend to be used to these days. I think the ‘manual’ process is useful for questioning the certainty of quantitative analysis, helping us to see it, not simply as ‘objective’, but rather as produced through a series of decisions, some human, some made automatically by machines. You make some good points about the reductive process of datafication, but I also wondered if a hard distinction between ‘quantitative’ and ‘qualitative’ might be challenged here – there are lots of ‘subjective’ decisions made in the ways data driven systems work, aren’t there?

    1. Thank you. Some really fascinating points raised – I agree with all the above. I think this goes back to a question I raised during the tutorial: are the types of data visualisations in Dear Data reflective of the intention and purpose of the data capture and is this at odds with the academic research work on evaluating improved learning and teaching methods?
      i.e. One can capture and record data with no intention of serving a practical purpose of improved outcomes but simply to convey what is (as observed)?

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