Student Performance Dashboard

|Matt Offord

I took the sample data set 2 and imagined these were data I had extracted from Moodle on the Principles of Management course that I teach. I also imported the data set into Google Data Studio. I found the Data Studio quite counter-intuitive to use. Like Excel it decides what your graph will look like and then you have to change it. I fing this quite frustrating as I use R for all my data analysis, and this requires the user to imagine the graph and programme R to produce it, so you only get the data and parameters you choose. Ultimately, however, I thought R does not produce the sort of graphics which look like a ‘dashboard’, so I used Excel (I thought it was simpler than Data Studio) to create these graphics. I think I could get used to Data Studio eventually.

I based my data dashboard on the sorts of questions I am usually asked in terms of student / course performance (I realise this cuts both ways). So initially I compiled attendance data as a psuedo ‘engagement’ measurement (it measures presence and this is rightly or wrongly conflated with engagement). I also thought it would be natural to assess performance via the grades, which I averaged across the 4 tests.

I also created a pie chart to identify the readers, those who had accessed the most pdfs (doesn’t mean they read them). This graphic is not very user friendly and on R (and probablt Data Studio) you can add the labels to the slices. But I noted the variation here was quite small. I then compared things like attendance, posting and VLE activity to see if there was any correlation with performance. In this data set only attendance is correlated with higher performances.

3 thoughts on “Student Performance Dashboard

  1. How would a typical graph produced by yourself in R differ from what’s possible here? I’m intrigued by the idea of the user “imagining” and “programming” the graph as you put it. In many ways, of course, the task to hand-draw data visualizations each week is about you imagining ways to represent data freed from the kind of software constraints that seem to be frustrating you in the above. But from your experience with R, is there more imaginative work that can be done with educational data and visualization than perhaps we’ve acknowledged?

    • Hi Ben, that is such an insightful comment because R’s popularity lies very much in the fact that there is more scope for the imagination than in a standard package like Excel or even Google Data Studio. That’s because (once you know the code) you can create whatever you like within a large range of visualisations, and they are journal quality. The R community are constantly writing new packages which push the envelope of what R can do, and packages are open source, free. I was reading in Edwards (2015) about the phrase ‘programme or be programmed’ and I think that’s the attraction of R, to a critical data analyst, R offers a huge suite of methods and it’s the programming equivalent of hand drawing, because you have to build it yourself.

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