Block 1 Summary

Learning and data have a complex relationship with each other. Learning involves a process of questioning and acquiring knowledge. Answers to questions or knowledge itself can be received from a “learned” person (the teacher). Yet, not all questions can be answered by existing knowledge, and unanswered questions drive observations and experimentations, which creates data that can give rise to new knowledge that supports learning in return.

While teachers’ use of data (e.g. field notes, journals, assessments, students’ records) to gain insights about their student has a long history, with learning being increasingly carried out online, we increasingly see institutionalised use of data analytics to monitor students’ behaviours within digital learning environments. Such development fuels the broader agenda to optimise learning for individual students (Tsai, Perrotta & Gasevic, 2020; Eynon, 2015). 

In Block 1 of CDE, I started my data collection journey by looking at the log data of my online activities from Chrome browser history, WhatsApp messages, Twitter history as well as email records, which is of similar nature to log data in learning management systems. This exercise confirmed my understanding of the reliance on digital footprints within learning management systems would fuel a behaviourist approach to education (Knox, Williamson & Bayne, 2019). For instance, a less-than-ideal pattern of participation in online learning activities could be treated as the ground for viewing behaviour modification as a way to optimise learning. 

In the latter part of Block 1, I started collecting data about my attention and thought process while completing the prescribed reading. In the process of collecting and visualising these data, I had to assign proxies or categories in order for a visualisation to be possible, hence I experienced first-hand the nature of data collection being a process of taking snapshots or creating proxies that represents a phenomenon. This provided a proof-of-principle to a previous saying I learnt – “‘raw data’ is an oxymoron.” (Gitelman, 2013). In addition, employing a “field-work” approach to data collection caused modification to my learning activity itself, making me acutely aware of myself being tracked. The act of recording my thought process while reading an article may have even converted that learning activity into a “pseudo-mindfulness exercise” (by focusing on my thoughts, accepting and recording all my thoughts and distractions). Overall, the data of my distractions patterns as well as thought process supported my metacognition, providing me more insights about my learning (Eynon, 2015).

Data-driven personalised learning requires pre-defined models of learning bahaviour set (Bulger, 2016), in order to support decisions on instructional modification and nudges to students who by such definition are regarded as less than ideal. Unavoidably, this approach favours a few learning bahaviours while shunning others. I would also argue that similar to the master-apprentice model of learning being idealised since aeons ago (Friesen, 2020), students have always been benchmarked against different ideal models, including their masters, the more diligent/capable/smart/hardworking/polite kid in the same neighbourhood, long before the advent of data-driven personalised learning. Perhaps this is precisely the sentiment that chartered this development in personalised learning.

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References

Bulger, M. (2016). Personalized learning: The conversations we’re not having. Data and Society, 22(1), 1-29.

Eynon, R. (2015). The quantified self for learning: critical questions for education. Learning, Media and Technology, 40(4).

Friesen, N. (2019). The technological imaginary in education: Myth and enlightenment in “Personalized Learning”. In Stocchetti, M. (Eds), The Digital Age and its Discontents. University of Helsinki Press.

Gitelman, L. (Ed.). (2013). Raw data is an oxymoron. MIT 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), 31-45.

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.

1 thought on “Block 1 Summary

  1. Good summary here Enoch, and interesting diagram of the learning process!

    ‘Such development fuels the broader agenda to optimise learning for individual students (Tsai, Perrotta & Gasevic, 2020; Eynon, 2015)’

    I know a lot of this research is characterised as ‘optimising learning’, but I do wonder if other agendas are also at play. Quite a lot of learning analytics seems to be directed towards preventing failure, which I often think serves an institutional agenda of improving retention rates, rather than actually ‘optimising’ individual learning.

    ‘For instance, a less-than-ideal pattern of participation in online learning activities could be treated as the ground for viewing behaviour modification as a way to optimise learning. ‘

    Indeed, and we have to ask questions about who decides upon the ‘ideal pattern’. I think there is a tendency amongst machine learning researchers to assume that ‘the data reveals’ the answer. However, I think that overlooks all of the human decisions that have gone into *producing* the data in the first place, and that is perhaps what our data visualisation task reveals best.

    ‘students have always been benchmarked against different ideal models, including their masters, the more diligent/capable/smart/hardworking/polite kid in the same neighbourhood, long before the advent of data-driven personalised learning. Perhaps this is precisely the sentiment that chartered this development in personalised learning.’

    Good point here – perhaps this is why data-driven systems seem to align so well with institutional and pedagogical practices? Maybe the real issue is not data, but measurement and competition?

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