Week 10 – Category Visualisation

Figure 1 – Visualisation

As part of governance, it seems as though the view that is desirable is that of averages and broad categories. As data becomes processed at each level the detail is removed from it.

Description of the Visualisation

Along the top of the visualisation, you have a horizontal line that is a linear timeline from the beginning of data capture to the end of capture. Every element on this line is also part of the large circles below.

The circles are split up into the following:

  • Left – University
  • Middle – Personal
  • Right – Work

Legend

Figure 2 – Legend

Reasoning

People who are governing and utilising data for that governance are looking for data that is more general. As the data is processed from its raw form the nuance or specifics of the data are generally removed. That is why I have gone with the above visualisation as it shows the administrator or school leaders areas of the students’ day and based on that they can see where the students focus is.

This iteration of the data could show that possibly students should be encouraged or nudged to do more university reading or spending more time outside. Then the governor’s can search out data that has been processed in another manner to show what the students are reading during that time or possibly if there is a reason for limited outside time such as lack of amenities.

There is always the possibility with any data that possibly it was the way in which it was processed that has removed some of its value for scenarios.

1 thought on “Week 10 – Category Visualisation

  1. bwilliamson

    This is a nice representation, and I enjoyed your reflections. Yes, there definitely seems to be something about broad averages and categories that is useful for the policymaker. However, many have argued that the breakthrough with big data in education is to gain granular, individual-level insights. That would enable interventions–e.g. nudges to improve engagement and drive up outcomes–in a much more “personalized” way. So a big issue in the governing with data space is how governing processes which used to mainly work at the policy level–as averaged-out performance or inspection scores for whole schools–have now moved down to the practice level, embedded in visualizations for teachers and software which is said to “personalize” the learning experience at the individual level. That’s attractive as a policy idea, but it relies on installing a complex apparatus of measurement and intervention–an information or data infrastructure–right into schools. I look forward to your final reflections!

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