Learning Spaces Data Visualisation
This week I decided to track my studying and learning spaces around the house for a period of 6 days. As I’m taking two course at this term, I decided to track the data also per course as much as I can. In certain cases, especially when I’m checking Moodle and the blogs the line is blurry a bit.
The question I’m trying to answer this week is :
Which studying space offers more focus and completion of planned task ?
Data Captured :
- Space type
- Course (Research Methods and Data)
- Time (am/pm)
- Focus Level
- Task Completion
The size of the circle reflects the number of times I used the space for studying. The choice of the space was truly random and based on what I was doing before I started to study or shifted my focus to my studies. It is basically what I felt like ! Usually I study or work outside the house in collaborative public spaces but lately the Covid-19 cases are on the rise so the spaces are within my home.
I decided to capture the different types of “university” related work and per course. I categorised the work as : assignments, blog reading and responding and reading suggested material. My own blog writing like this one is categorised under assignment work. Here are some reflections of what I learnt about my learning spaces.
- I do almost as much studying in bed as I do at my home office / desk. They are in two separate locations in the house.
- I tend to complete more tasks with the highest focus in the office than any other space.
- In the afternoons, I sit on the couch (TV room) and do some work but level of focus and completion is definitely less given the distractions from the TV, family members, dog and being tired after a long day sitting at the office desk.
- I did more “assignment” word for the Critical Data course vs. more “reading” for Research Methods given that we had two assignments this week in Critical Data while an optional one for Research methods.
- In general, I’m not able to finish the reading work needed for research method and that’s inline with my lack of focus in the course readings – I’m finding the reading more abstract and too much to complete
- Most of the studying is in the evening and that’s inline with my schedule of last week data capturing and also because I do my day job in between.
- I don’t do much reading at home office. I guess this is related to being sitting down and I personally prefer to be more relaxed when I’m reading.
Overall this was an interesting reflection to me because I thought I don’t like to study much at my home office as I wanted to separate my studying from work. However, it seems it is the most efficient learning space.
What I didn’t captured during this week was how I was feeling or additional information like: noise level, stress, distractions and over all mood. I thought about capturing that data but I felt it will be too much information and I was worried about the actual visualisation exercise more.
The question that comes to mind here, how much we design our data capturing tools and methods based on a real understanding of the question we are trying to answer or on how complex the data sets can be which will definitely impact not only the outcome but on how we understand and analyse the data?
Another very interesting visualization. I wonder whether you’ve thought about what a software application might ‘infer’ about you from tracking these spaces and practices of studying? Especially given your final reflection about tracking noise, distractions and mood. These seem like important contextual and emotional factors that infuse any space, but would be imperceptible to software. Packages like learning analytics claim they build a ‘model of the student’ but how complete could this ever be?
By the way, for software personal tracking I used a food tracking application.
Thanks Ben…analytics are always selling perfect, model, ideal, better, improved …etc being in the IT world that was our language ! I would like to think about analytics as recommendations and insights of what can we learn more from data but depends on how we are linking the measurement and outcomes. I would like to ask: What are the qualities of a model student?. First we need to understand what we are measuring and then analysing the outcomes to understand how they link the predefined qualities / reference. So to answer how complete ? I would say to a certain extent and depends on the design of the learning analytics selected / presented.
Hi Dima, like you I also felt that I should be recording more variables but that would have made my visualisation too complex. Perhaps a machine would be better at capturing a lot more relevant data? But maybe you subconsciously recorded things like mood or noise which a learning analytics system wouldn’t be able to capture at all in which case your tracking might be more meaningful…