Hello world!

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

Reference

Lupi, G., & Posavec, S. (2016). Dear data. Chronicle books.

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