Learning with data

Visualization 3. How do I spend my time?

This visualization tries to assess the amount of time I spend learning. It helped me to learn about my own learning.

More than 50% of the time I spend teaching

It is a key way how I receive my income. However, data is not neutral, and my personal imprint needs to be considered (Knox et al., 2019). I collected data from Wednesday to Sunday. If I had gathered it from Monday to Sunday, the percentage would have been probably even higher.

Almost 25% of the time I spend learning

I expected a lower percentage of my time. It surprised me. Learning is highly important to innovate and make sophisticated decisions (Wiggins, 2018, cited by Eynon, 2015).

Visualization design choices: bars were chosen to show a clear difference in time spent on different activities. Colours were chosen for bars based on how much do I enjoy while doing a particular activity. Enjoyment plays a crucial role in the continuation of activity (Stevens, 2000, p. 601).

Key finding: fourth of my time, I spend learning. This is a sufficient percentage, and my concentration might need to be shifted towards physical activities or personal needs.

Learning with data

Collecting data about personal activities can help find necessary data related to learning (Eynon, 2015, p. 407) and encourage conversation between students and facilitators, which is crucial in learning (Stocchetti, 2020). It can provide the necessary feedback (Ifenthaler & Erlandson, 2016, p.1). My third visualization shows the necessary feedback to me about where I spend my time.

Not all findings from data can be linked to learning. What parts of experiences, processes, and outcomes can be related to learning need to be carefully assessed (Eynon, 2015, p. 408). For instance, my own physical activities analysis showed that it has little or no influence on learning.

Using numbers in a very complex field such as learning can be challenging. It can result in many different consequences for individuals. For example, it can diminish creativity (Beach & Dovemark, 2009), or encourage learners to concentrate on data rather than learning itself (Wise et al. 2013, cited by Eynon, 2015). Therefore, various factors need to be carefully considered (Eynon, 2015). For instance, how motivation or enjoyment can get a specific value in machine learning models? Black-boxes (Tsai et al., 2020) can not be left. Various legal and ethical issues need to be addressed too (Eynon, 2015). It was not an issue for me because I was analyzing my own data.

There are multiple advantages from data that can be used for better learning, such as enhanced motivation, additional support, informed learning choices, and enhanced meta-cognition (Eynon, 2015). The past three weeks’ visualizations helped me to realize that physical activities do not have a significant effect on my learning and that I spend a sufficient amount of time learning.

However, there are multiple disadvantages too: closing down creativity or alternative ways of learning, changing self-concept, shaping educational opportunities. Various stakeholders need to be aware of it (Tsai et al., 2020). I have experienced changing self-concept regarding the relationship between physical activities and learning. I do trust data, however, a critical evaluation of data and its visualizations are essential! Data literacy is an important skill (Knox et al., 2019).


Beach, D. & Dovemark, M. (2009). Making ‘right’ choices? An ethnographic account of creativity, performativity and personalised learning policy, concepts and practices. Oxford Review of Education, 35(6), 689-704.

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

Ifenthaler, D. & Erlandson, B.E. 2016. Learning with Data: Visualization to Support Teaching, Learning, and Assessment, Technology, Knowledge and Learning, vol. 21, no. 1, pp. 1-3.

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.

Perry D Wiggins. (2018). Metric of the Month: Learning Days Per Employee.,, 2018-09-10.

Stevens, M et al., (2000). The Groningen Enjoyment Questionnaire: A measure of enjoyment in leisure-time physical activity. Perceptual and motor skills, 90(2), pp.601–604.

Stocchetti, M. (Ed.). (2020). The Digital Age and Its Discontents: Critical Reflections in Education. Helsinki University Press. doi:10.2307/j.ctv16c9hdw

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

2 replies on “Learning with data”

‘More than 50% of the time I spend teaching’

I suppose one useful question here might be about the distinction between teaching and learning, and whether you think they are mutually exclusive. Categorising in the way we are doing makes this hard distinction, but it may not reflect some of the nuance – I certainly feel like I’ve learnt quite a lot from teaching this course so far, for example! Maybe your teaching concerns something quite different from the subject matter on this course, but perhaps you learning *something* as well.

Is this one of the fundamental issues with assuming that data can represent learning: that we are either learning, or not? Does learning really happen in such a discrete way?

‘Key finding: fourth of my time, I spend learning. This is a sufficient percentage, and my concentration might need to be shifted towards physical activities or personal needs.’

Sure, but I can’t help but be pleased with that result 😉

Thank you, Jeremy! I just wanted to ask, do I need to answer those questions that you have raised in my blog? So for the future, in my blog posts, I will need to question more things that I write, not try to oversimplify everything like I have done here, right?

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