Block 1 reflections

For this block I tried to visualise three different aspects of my ‘learning’: reading, motivation and interactions. By collecting, visualising and analysing my own data, I was hoping to gain deeper insights into the relationships between data and learning.

One of the challenges I faced every week was to decide which variables to consider for my visualisations. Is it best to collect as much data as possible or should I trust my instinct and log what I feel is relevant for each theme? It became clear that more sophisticated technologies such as learning analytics systems face similar issues. Despite being able to collect and analyse more data, there are many aspects of learning that can’t be captured or analysed. Eynon (2015, p. 409) also warns of the danger that ‘[h]ours spent revising, numbers of words written per day, multiple choice questions answered in half an hour, can all become the most important metric, rather than the quality of the writing, the mathematical thinking or the cognitive process.’

While I recorded data, it realised that the data collected by me is in no way comparable to big data (BD) as ‘BD promises ultrarapid, updatable profiling of individuals to enable interventions and action to make each individual, their thoughts and behaviours, actionable in a variety of ways’ (Thompson & Cook 2017, p. 743). In comparison, my efforts were slow and selective but it raised the question whether big data is the only way to paint a meaningful, rich picture of the learner. During my self-recording, I often felt that it was context and personal circumstances that had an impact on my actions, yet these variables are difficult to measure.

During the last three weeks I frequently asked myself whether collecting data can somehow improve how I learn. In education, after all, learning analytics promises insights into learning that would otherwise be unobtainable (Knox et al., 2019).

One of the promises is the ability of students to have a greater sense of agency. Data is thereby used to make informed decisions during the learning process (Tsai et al., 2020). Interestingly, however, Tsai et al. (2020, p. 562) surface that student agency may potentially be diminished ‘through constant surveillance in online learning environments.’ I certainly felt conscious of my actions being recorded (albeit by myself) and could imagine how constant monitoring may have an effect on how I behave. While it could lead to increased self-motivation, I could also see how my focus could be on simply completing tasks without caring too much about how well I performed in them.

Digital data is often seen as a solution to various problems in education (Selwyn & Gašević, 2020). For data to be used in order to enhance ‘learning’, I suppose we need to assume that how or what we learn needs to be improved. Although I am not able to offer a definition of learning, I believe that it is very personal. So the thought of learning being tailored to each student and offering them the best possible ‘learning journey’ seems intriguing. Reflecting on this block’s literature regarding personalisation, however, there seems to be a conflict between personalised learning systems being beneficial to students and teachers, and having the potential to ‘disempower through opaque processes and prescriptive formats (Bulger, 2016, p.19).

What has become clear during this block is that there are many conflicts between data and ‘learning’. I’m hoping to continue to explore these conflicts along with the relationships between data and education during the next block.


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

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

Selwyn, N. & Gašević, D. (2020). The datafication of higher education: discussing the promises and problems, Teaching in Higher Education, 25:4, 527-540, DOI: 10.1080/13562517.2019.1689388

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

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

1 thought on “Block 1 reflections

  1. Your commentary offers a very good overview of some of the key arguments emerging from the course readings, related very effectively to your own observations and reflections from completing the data visualization tasks. It’s great to see you grappling with some of the highly optimistic claims about the power of educational analytics, and their equally problematic functions and potentials. You’ve acknowledged, too, that ‘learning’ is a highly contested concept, open to multiple interpretations and conceptualizations. To take a learning analytics approach to learning may be to prioritize quite limited ways of conceptualizing learning, with significant effects for learners themselves if reductionist representations are used to reshape the subsequent course of their education.

Leave a Reply

Your email address will not be published. Required fields are marked *