This week’s drawing I decided to collect qualitative data about my thoughts in reading the paper written by Eynon (2015). In reading the article, I employed a “thinking-out-loud” approach and recorded the thoughts I had, while logging the time points at which I had such thoughts. After the data collection, I developed 5 categories to describe my thoughts, and plotted them as a simple one-dimensional graph. This drawing may have a similar look to week 4’s drawing which aimed at visualising the pattern of my distraction while reading – the data collection method was very different.
On week 4, I employed simple logging by immediately categorising the distraction at the recording stage, resulting in data like “11:40 – non-work, 11:42 – data collection, 11:45 – data collection, 11:55 – work”.
However, for week 5, the data collection was descriptive, leading to data like “5:41 – am I sabotaging my reading by making my data collection this way? amused by myself; 5:44 – any way I can experiment with my data collection exercise?”. The categories were developed after reading through the data once.
For the past week, my data collection focused on the big picture of my learning, providing additional contexts such as work and personal life. This week, I decided to go more microscopic and manually record the distractions I experienced while reading the Bulger (2016) paper and the Tsai, Perrotta & Gasevic (2020) paper.
The way I recorded distractions was similar to having a time sheet, where I logged the time I started reading the paper concerned. While reading, I also recorded the timepoints whenever I perceived I I got distracted. Whenever the distraction caused me to consciously put down the paper and did something else, I logged the times at which I put down and pick up the paper, and also record some qualitative details about the distraction (or rather, “derailment”). The distractions were categorised based on their nature (work-related, non-work-related, etc.).
I felt my scientist self also prompted me to experiment a bit in this data collection exercise. I have tried multiple times to get used to reading journal articles on electronic devices, as opposed to printing them out. I tried this during IDEL’s week 1 but then I fell miserably. The “experimental conditions” were as followed:
Bulger (2016): reading on OneNote with my hybrid laptop, sitting on my bed
Tsai, Perrotta & Gasevic (2020): reading on printed copy, sitting on my bed
However there were also limitations to this experiment: (1) Bulger (2016) was significantly longer than Tsai, Perrotta & Gasevic (2020); (2) I started reading Tsai, Perrotta & Gasevic (2020) much later than Bulger (2016) despite being on a different day.
The design rationale of the visualisation is based on the literal meaning of “distraction” – which is the phenomenon of my attention being taken away from what I was trying to do (Oxford Learner’s Dictionaries). Hence I used a time axis to symbolise the task at hand, and visualised my distractions as a curly line that points out from the time axis (i.e. my thought leaving the task at hand) and gets dragged back towards the axis (i.e. regaining attention on the task at hand). I also used icon as proxies for qualitative details about my derailments.
A common pattern is that I tend to have a cluster of distractions at the beginning of starting a task. It also shows that I experienced more derailment in reading Bulger (2016), including having to make a note of the reference I located for my manuscript, as well as deciding to send Bulger (2016) to my work colleagues.
In contrast, I experienced less derailment while reading Tsai, Perrotta & Gasevic (2020). This is likely caused by my being conscious of data being recorded, and I was working harder to block out distracting thoughts. Also towards the end I dozed off. This may suggest reading at 11pm would be counterproductive.
Overall, I have used this week’s data collection and visualisation exercise to reflect on my learning behaviour and attempt to derive insights from it. It taught me to expect distraction clusters at the start of a task, and hence additional effort is needed at the beginning of a task so as to make sure the task can be completed.
This is the data drawing I produced for week 3, visualising the timing of my activities within the Critical Data in Education (CDE) course, in the context of work and other commitments. The data were collected using multiple ways: Google Chrome history, Twitter history, Signal app history and by recall. The drawing paints a picture of how I fit the activities of CDE on top of my existing commitments (hence the placement of CDE activities on the top of the timelines) and significant transitions in my life (starting a new relationship!).
This drawing provides a broad overview on my overall well-being. It seems to show that I managed some balance between my work, study, relationship and family. It may suggest I am doing okay in terms of social wellbeing (e.g. staying in touch with family/friends, spending substantial amount of time communicating with my new girlfriend).
I felt this type of data collection exercise (i.e. time tracking) would be a good exercise for students’ wellbeing, providing students not only the information on how much time they have spent on studying, but also whether they have spent decent amount of time to maintain their social wellbeing.
Below is my attempt of recalling what I did at work on the past few days:
Data source: no actual data recording has been done – rather, the data was obtained though looking at email and whatsapp trails.
The marking is clearly not to scale – hence this visualisation is best described as semi-quantitative. And I think it shows the limitation of self-reported findings, which can be somewhat impressionist in nature.
Red colour denotes the part of my work that is directly related to e-learning initiatives at my workplace; while blue colour ones had a less direct connection with that initiative.
Why did I use straight lines and curly lines? It wasn’t a very conscious decision, but it turned out I enjoyed the activities denoted by curly lines more than the straight lines.
I have had a love-hate relationship with data. As a biomedical scientist, doing experiments, obtaining readings, entering data, pressing the right buttons for descriptive statistics, Student’s t-test or ANOVA were my day-to-day routines. Getting a p-value below 0.05 had always been the short-term goal in my research. I have always thought I am well acquainted with data analysis, and consider myself rather good with data.
IDEL’s weeks 10 and 11 last semester got me to think more deeply about the nature of data. Apart from the social impact of big data, I was reminded of data’s nature as a proxy of reality. It was something I was conscious about, but have never put it into words. It reminds me of my past experience with biological experiments – as shown in Figure 1, imagine data collection occur at 10, 15 and 60 minutes after addition of a stimulus, both scenarios would shown below would lead to the same readings: 10 minutes – 2-fold; 15 minutes – 4-fold; 60 minutes: baseline. However, the reality is that those two scenarios were very different, where the one on the right hand side dropped down to baseline at around 30 minutes.
Figure 1. Sketches of typical cell signaling activity kinetics
Having gone through IDEL’s weeks 10 and 11, I became convinced that this course (Critical Data and Education) isn’t really about learning the critical new techniques for data analytics in education, but rather about critically examining and understanding the way data impacts the education sector and education itself. In once sense this also reminded me of the core reason why I chose MScDE programme in University of Edinburgh instead of other universities – I wanted to develop a critical insight in making sense of digital education instead of learning instructional design and e-learning development (which I have learnt through my job). Likewise for CDE, I wanted to gain a critical insight in data and education.
From a utilitarian point of view, I am most interested to explore more about learning analytics. At my current workplace, we have a relatively “clean” state with using data analytics, which is currently limited to video watching logs and Moodle quiz logs. Through CDE, I believe I can gain the confidence and knowledge to be able to take part in the bigger discourse on using big data and analytics in my institute.
The topic that inspired me the most so far is data visualisation. I have always been conscious about the quantitative quality of data visualisation, where different stories can be told from the same data. Once can choose to show the data in scale (Figure 2 left) or to exaggerate the difference (Figure 2 right). In reading Lupi and Posavec (2016), their use of colour coding shows their attempt to use nominal scale and ordinal scale approaches to describe phenomena that are “qualitative”.
Figure 2. Importance of scale
Lupi, G., & Posavec, S. (2016). Dear data. Chronicle books.