Week 4: a week of motivation

Data Visualisation: motivation

This week I decided to track my motivation for studying. It was definitely more difficult than simply recording tasks or durations but I actually found the daily self-reflection very interesting and hope it can be beneficial for my learning. To decide which variables to include was probably the most challenging bit. Motivation can be affected by so many internal and external factors, it was hard to decide what the most important variables are. Before this exercise I always thought that I’m more motivated in the mornings than in the evenings so I included times of the day. My visualisation, however, doesn’t show a clear correlation between motivation and times of the day although a bigger sample would certainly be useful.

To better understand motivation, I explored Ryan and Deci’s (2000) Self-Determination Theory. It describes how three psychological needs – competence, autonomy and relatedness – need to be present to enhance self-motivation and mental wellbeing. Looking at my visualisation, I can see how feeling competent has potentially influenced my motivation. I struggled with the Friesen article and had to read it over several days (Monday-Wednesday). On the other hand, I found the Eynon article much easier both in terms of content and language, and felt motivated when I finished reading it. The reason for choosing the bars was that I could easily show the change in motivation. Even when I wasn’t motivated at the beginning, I sometimes got more enthusiastic if I felt engaged with the task.

What I realised when drawing was how subjective my data visualisations are. I select what I’m logging because of what I think makes sense or because of the number of colours or shapes I want to use. While learning analytics systems may have the capability of recording more data, there is only so much you can express on a dashboard, for example. And then there is the question of who chooses the data. If it is mainly software engineers as surfaced in Selwyn and Gašević (2020), how can we be sure that was is being collected, analysed and presented is beneficial to students’ learning?


Ryan, R. & Deci, E. (2000). Self-Determination Theory and the Facilitation of Intrinsic Motivation, Social Development, and Well-Being. The American psychologist. 55. 68-78.

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

4 thoughts on “Week 4: a week of motivation

  1. Hi Susanne,
    As you say, it’s certainly interesting to try to capture the qualitative aspects of learning but oh, so difficult to know how to quantify them (and know what is behind all the decisions you make in these exercises).

    • Indeed, Tracey! I was wondering how tech could help with quantifying these aspects. Could measuring heart rates and facial expressions really make a big difference in understanding how we learn? I’m not so sure.

  2. What an inventive idea for your visualization. It raises a host of really important questions and issues — as you say, there is subjective bias in what you choose to measure for yourself, but so too in the decisions that engineers and learning analytics experts choose to measure. What is especially interesting though is that you’ve turned to Deci and Ryan on motivation to try to figure out how to quantify something that is deeply personal, emotional and psychological in nature. This is the kind of thing that a learning analytics practitioner would do. They select certain theories of learning, or factors that are ‘proximal’ to learning (such as motivation) as the basis for designing measurements and instruments. That reinforces the importance of seeing learning data not *just* as digital artefacts of behaviours or actions, but as the translation of specific human features as defined by psychological theorists. Datafication of learning therefore privileges psychological rather than social explanations of learning and education, and often highly individualized explanations too. I wonder which psychological factors that are ‘proximal’ to learning, besides motivation, underpin much of the learning analytics field?

    • Thank, Ben! That’s really interesting. I think it’s very engrained in us to try and quantify anything related to data. I will try and investigate further to see which other psychological factors are used to underpin learning analytics.

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