Tracking emotions before and after learning activities
For this week, I decided to collect my emotional status before and after doing any university work be it reading course material, participating in blogs / discussion forums or working on assignment for both courses that I’m taking Research Methods and Critical Data. The question(s) I wanted to investigate is :
How the emotions change after engaging in a learning activity?
Is the emotional change linked to the emotional status before
studying or the learning activity itself?
I have to say that this week was an emotionally stressful week for me with some personal stress and sad feelings triggers that I carried through out my working week. My week starts on Sunday; so, I captured data from Sunday to Friday logging the before and after emotional status of any learning activity. The following are the data captured and the legend for the data visitation thereafter.
After collecting the data, I tried to group some emotions under the one to minimise the disparity of the data. The grouping presented in the color coding:
- Sad and guilty
- Impressed, motivated and engaged
- Tired and confused
- Frustrated and angry
- Stressed and anxious
- Happy, content and relaxed
My inspiration of the circular representation of the data visualisation was the expression “emotional roller-coaster”. According to the Collin Dictionary, emotional roller-coaster is defined as “a situation or experience that alternates between making you feel excited, exhilarated, or happy and making you feel sad, disappointed or desperate.”
Studying, while working and taking care of my family in a confined home within a pandemic situation, does feels like a roller-coaster. I tried to differentiate between the two courses and the type of study. I definitely needed more data and more emotionally variable weeks, but the following is the visualisation outcome.
My reflections of the data:
| I’m definitely having a stressful week. It was a starting emotion for many activity this week and I can see that the Research Method assignment was not making it any easier. In many instances, I ended up being more tired / confused or anxious. |
| Working on the Critical Data course in general improved my emotional status as most of the “after” status were Green and Pink. |
| Working on something I liked definitely improved my emotional status and motivated me to do more work. |
| The days that I felt sad, studying didn’t make it any easier. |
| Working with Blogs in general improved my emotional status. I linked this to the feeling of being engaged and belonging to others. In general, being with people does improve my mood and emotional status with one exception. |
| If I had a low mood, studying can help me get distracted and lift my mood depending on studying type and activity. |
It was hard for me to measure my own emotions as it was not a black and white data collection activity and in many cases was dependent on my own interpretations of how I feel and state of mind. Emotions can have a direct impact on learning whether positive or negative impact but it is not as straight forward. Many factors come in the equation. According to a UNESCO publishing from the International Academy of Education titled Emotions and Learning (Pekrun, 2014), and I quote:
“Positive emotions do not always benefit learning, and unpleasant emotions do not always impede learning. However, for the vast majority of students and academic learning tasks, enjoyment of learning is beneficial.”
If it was hard to identify or influence my own emotional status, how would machine learning or learning analytics help influence learners towards improving earning capabilities and outcomes? I will conclude with a quote from Knox et al. (2019) of what could be a claimed feature of learning technologies:
The ‘learner’ is now an irrational and emotional subject whose behaviours and actions are understood to be both machine-readable by learning algorithms and modifiable by digital hyper -nudge platforms.
References:
- 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.
- Pekrun, R. (2014). International handbook of emotions in education. doi:10.4324/9780203148211
Your dataviz and commentary certainly highlight the difficulty of tracking and representing something as complex as the emotions as numbers and graphics. In reducing the categories, did you feel you were going through a necessary process of reduction in order to make your ‘rollercoaster’ of feelings representable?
For sure… less is more in this case. I started with many more ideas and data point in my own brainstorming of the activity and found that how I would struggle without the use of a visualisation tool or something. I compared to Dear Data week 11 “week of emotions” but they definitely had more interactions and areas to measure. Being restricted to the “learning” dimension made it even harder.
Hi Dima,
After doing this, do you think it will have any influence on how you will structure your study times/activities, or is it not possible to do that with the other demands on your time?
Its very hard to do soo especially being at home and everything is overlapping … work, home, family time, study …etc !
I love your emotional rollercoaster, Dima!
I’ve been thinking about trying to track my emotions too, but, frankly, I felt a bit apprehensive about the whole idea, first off, because the range of emotions can be vast, and they could be difficult to catch and define. As far as I know, being able to define your emotions is a major factor in EQ. So, undoubtedly, the question you raised regarding the ‘ableness’ of tech to do the job makes a lot of sense to me.
I have to say it was not easy!