My overall experience with this task was one of confusion and a simple question, what do I show that is relevant? The answer to that was always very close to this is not showing anything or if it is what real value is it showing other than numbers on a screen. It showed me in the same way the literature has been explaining that gleaming any meaningful information or understanding from dashboards does require more of a background to the pupils instead of just the numbers and a way to be able to critically analyse that data.
We were provided with two sets of data to work with and create some form of a dashboard, I decided to work with Sample Data 2 as Sample Data 1 was missing a lot of background information. Sample Data 1 had only numbers and it was difficult to understand if each number was associated with a particular section and if that was the case why some lines did not have the course ID. (Figure 2)
As can be seen from Figure 2 it seems as though this information is in regard to “course 14” but with no definitive information on that it would be wrong to draw that conclusion.
As can be seen from Figure 1 I decided on straightforward graphs as I was unsure about how to display certain correlations I thought would be good. I struggled with how best to explain in a graph / dashboard that if a user logs in less to the platform and performs poorly show the teacher that information. At the same time, I was conflicted about trying to do that as that has been shown to not be accurate, how much someone interacts with a platform does not mean that they will do better or worse in an exam.
In my exploration of this I noticed that I was drawn more to the exam results than any other item on the CSV file. This idea seems to be one area that is the same throughout society we put so much emphasis on exam results that they are all that matter at the expense of everything else. If there was more granular information about a particular subject or task it is quite possible that this would be more valuable as it would be able to show knowledge increase over time and not just one point in time as exams do.
While these files had a certain set of data it made me think about what data should be collected to make the dashboards more valuable and it became rather complicated. As you start to measure you start to think about measuring everything but as you do that you encroach on people’s freedoms and is an education dashboard a valid reason to do this? Probably not.
“it made me think about what data should be collected to make the dashboards more valuable and it became rather complicated” — this is a good observation. I know there has been some heated discussion in the learning analytics community about whether to include personal, sociodemographic data (ie “attributes” that a student can’t change) or only to include data on “behaviours” (which a student could change). Including attributes could lead to gender-, race- and class-based profiling (e.g. if the analytics identify working class Black girls to underperform). But if you exclude attributes, and assume students *can* change their behaviours, might you ignore all the structural factors that result in gender-, race- and class-based forms of inequality in the first place, and which shape how students behave and perform in education? So even deciding which type of data to include or exclude is complicated. Pleased to see how you are grappling with these issues first-hand.
I think, like you, I felt I had data but nothing that I really found interesting, and when I looked for correlations, it still didn’t tell me anything useful. I’m not entirely surprised.