Lindsay's Data Visualisation Blog

5 – Steps and motivation

Context

I’ve been thinking for a number of weeks about the impact of learners’ emotions and self-motivations on their learning and how that could be useful to a teacher in terms of finding trends in that data. It has been a bit of a rabbit hole though, and feels incredibly complex to unpick and measure. Bower (1992) says that “emotion is evolution’s way of giving meaning to our lives … [which] … are ordered and organised by our needs, motives and concerns” (pg, 3).  Studies have made specific links between cognitive processes such as attention, learning, memory and problem-solving being affected by emotions, and so it feels like an important perspective to grapple with (Phelps, 2004; Um et al, 2012). Tyng et al (2017) highlight that the impact of the move to digital learning experiences (from traditional face to face) “may induce various emotional experiences in learners” and therefore argue for careful consideration in educational course design.

Kleginna and Kleginna (1981) offer one perspective to a broad ‘definition’ of emotions, which includes that they motivate behaviour that is often but not always expressive, goal-directed and adaptive. It is this aspect that I chose to focus on for this week’s data visualisation.

I tracked my steps using my fitbit and the app, and manually tracked my points of motivation in a notebook. Also, last week I was involved in a competition with colleagues from work which tracked my steps, and ranked me in comparison to my colleagues. I wouldn’t say that I am a particular competitive person, but I truly believe that this had an impact on my motivation.

Goals

My goals for this week were:

  • Personal goal: hit 10,000 steps daily
  • Group goal: don’t be last in the group of 10

Design/layout

I chose a horizontal bar chart, as I felt that this represented distance. If a teacher was accessing data like this, it might better represent the data than say scattered info or even column chart (which is what I’d usually call a bar chart). I also thought the layout could have allowed more than 1 person’s data to be seen at a glance here.

Motivation

The bubbles show the ‘bursts’ of motivation that I felt I had. Although I tracked this manually, it is likely that by using the fitness tracker in a depth of detail about number of steps in specific periods – each of these bursts would be accompanied by an increase in pace/consistency of steps. From a teacher’s perspective perhaps data like this would be more useful about the group rather than the individual, eg say a task was set and there was immediate engagement ‘bursts’ from a number of learners then it would be useful to reflect/investigate why that happened. I wonder if instead of the interventions that I had explored last week this is also about reflections on engagement (although I’d argue that’s fundamental in everything we do).

Rank order

This section has been in and out of the analysis all weekend – I have a thing about rank order. I feel there are elements of it that don’t co-exist comfortably with a learner-centred approach. That doesn’t mean that I don’t think rank order can be used as part of decision making around learners of course, just that I feel it should be treated cautiously. Baird (1997) did conclude that the evidence in his study on assessment estimates that institutions were good at rank ordering their learners.

Anyway, my point here is that that the system I used ranked us, because it was a competition. Perhaps there is echoes here of the point that Williamson et al (2020) make around some data practices ensuring consistent “improvement through measurement, … winners and losers and the attribution of quantitative value” (pg. 353). That resulted in self-motivation, I guess it tracks back to the point that I made last week about the value of a combined reflection on learner data – between the learner and the teacher would be more valuable here if taking the view that each learner should be working towards their best rather than trying to be the best.  

References

Baird J A (1997) Teachers Estimates of A level Performance, Guilford, Internal Report RAC/763, Associated Examining Board.

Bower, G. H (1992) ‘How might emotions affect learning’ in Christianson S. V. (ed) (1992, 2014) The Handbook of Emotion and Memory: Research and Theory (Psychology Press: NY)

Kleinginna, P. R. Jr., and Kleinginna, A. M. (1981) ‘A categorized list of emotion definitions, with suggestions for a consensual definition’ Motiv. Emot. 5, pp. 345–379.

Phelps, E. A. (2004) ‘Human emotion and memory: interactions of the amygdala and hippocampal complex’ Curr. Opin. Neurobiol. 14, pp. 198–202.

Um, E., Plass, J. L., Hayward, E. O., and Homer, B. D. (2012) ‘Emotional design in multimedia learning’ J. Educ. Psychol. 104, pp. 485–498.

Williamson, B. Bayne, S. Shay, S. 2020. The datafication of teaching in Higher Education: critical issues and perspectivesTeaching in Higher Education. 25(4), pp. 351-365.

2 Comments

  1. Permalink

    ‘Bower (1992) says that “emotion is evolution’s way of giving meaning to our lives … [which] … are ordered and organised by our needs, motives and concerns” (pg, 3).’

    Interesting quote – I don’t know the text, but it certainly seemed to convey, in a useful way, how emotions are tied to learning. I think the areas of psychology I’m less convinced by are those that place too much emphasis on ranking emotions, or suggesting there are more advantageous emotional states than others. I do think it is also pertinent to reflect on emotional states currently, with the shift to online.

    ‘From a teacher’s perspective perhaps data like this would be more useful about the group rather than the individual, eg say a task was set and there was immediate engagement ‘bursts’ from a number of learners then it would be useful to reflect/investigate why that happened. ‘

    Yes, I can see how that group, collated data might be really interesting. I guess I’d wonder if one could really see consistency across different groups of students, however.

    ‘the value of a combined reflection on learner data – between the learner and the teacher would be more valuable here if taking the view that each learner should be working towards their best rather than trying to be the best. ‘

    Interesting point here. I suppose the idea of ‘working towards *their* best’ might be better aligned with self-tracking, and comparing ones progress with previous progress.

    Reply
  2. dkandalaft Permalink

    I like how you linked motivation to your activity… did you try to capture any other data related to these “bubbles” of motivation ? your thoughts at those moments, what you were doing or what you were seeing ?

    Reply

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