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Week 11 – University & Personal Life Visualisation

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Figure 1 – Visualisation

Following on the governance block I have focused here on showing general information on my university life and my personal life. From the readings for this block, it seems as though the view decision makers have is that of averaged, abstract data that can show little about who the person is but give broad indications of what is occurring.

Description of the Visualisation

Everything to the left in the visualisation is related to my university studies and everything to the right is covering a general area of my personal life.

On the University side I have split it into three areas which are:

  1. Reading – course specific reading
  2. University Admin – checking tasks, gathering all the readings, reading feedback
  3. Reflections, Tasks – Visualisations, writing reflections

On the personal side I have three areas also:

  1. Food – Eating, preparing, cleaning up food
  2. Exercise – Walking, yoga, etc
  3. Relaxation – TV, playing video games, reading books

Legend

Figure 2 – Legend

Reasoning

Through the readings on governance and over the course of this module I have become aware that decision makers are looking for vague or general information on students, teachers, schools, districts, etc. The closer to the student a viewer of the data is the more detailed information they seem to require, meaning teachers want to have as much information as they can to hand to make a decision on a given student, an administrator wants to see how a class is achieving and district supervisor wants to see how a school is performing.

With this visualisation while it is showing my data in one-hour segments, it would be possible to utilise the same visualisation to show data for an entire class or school. What I have seen as important over the course of this module is to reduce training on how this data can be interpreted and one way to do that is have the same visualisation, so people understand how to read it and then have the visualisation be able to represent a student, a class, or a school. While this has its drawbacks in so far as removing a lot of granularity to the data it ensures that training can be given, and the pitfalls can be called out by the training.

The idea also with this visualisation is that it gives the decision maker some method of being able to express certain areas where they want to see improvement in such as ‘we want people to spend more time reading, how do we achieve that?’ Or ‘we want to ensure students are accessing more exercise as this improves student retention, how can we encourage that?’

Block 3 – Reflection

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This block and the associated readings helped me understand more closely what the data is that decision makers have used previously and currently in governing education. This data in some form has played a key role in education for a long time, in the past ‘government education departments were key centres of calculation that were able to collect and aggregate data on school’ (Williamson, 2017) but in recent years large multinational technology companies have become the key centres of calculation and a form of the data is then handed off to the relevant government departments.

I question the methods in which data are currently used in governing and within that the impact the data gathers and data processors have on the resulting data.

When I look at the methods of data collection and representation that have been described in the literature, such as databases, inspections and reports (Williamson, 2017), the term that comes to mind is abstraction. As you add more and more entities or steps in the processing of data that data is more and more abstract from its original form. Abstraction in the form I am most familiar with is programming, within programming these levels of abstraction are for the most part ‘black boxes’, it depends on the situation, but you might not be able to peer inside and see what a program is doing. The output from these black boxes can have little context as you cannot peer inside the box and understand how the output came to be. The same stands for the output of education data, behind the data is a student and possibly their future career but as the levels of abstraction are applied to the data the detail of that student is removed and decisions are made solely on the numbers and not on any extenuating circumstances that might be at play. Case in point the inspectors within Ofsted that could not show that a school was improving just by the data they were asked to collect, because of the data being limiting and not being able to show the context a school could have improved but the data be unable to show that (Ozga & Williamson, 2016).

In ‘Data frontiers and frontiers of power in (higher) education’, Prinsloo (2020) shows that organisations in the Global North are using their considerable influence to push countries in the Global South to adopt technology so that they do not miss out on the Fourth Industrial Revolution. Governments and citizens of the Global South do not want to miss out on being apart of the next revolution and so are keen to work with organisations in the Global North that can help them catch up on the Global North. One of the issues with this approach is that most of the prominent organisations are from the United States and their platforms are designed for the US education system, by countries within the Global South adopting these they also adopt the pedagogical approaches that these systems have. The dashboards that are produced as part of these are also from the view of US education policy meaning that not only is the technology being developed outside of these countries, but they also have no say in the education policy that come with these systems.

As countries are facing increased pressure to adopt EdTech, one such method that these countries have of pushing back on these EdTech platform providers is to put regulation in place that would ensure that they adapt to the country’s rules. It is very difficult for one university or one school to push a technology company to adapt the platform for their requirements, but a government can exert such influence that the platforms make the necessary changes. Governments place regulation around a large number of areas of the economy but one such area lacking regulation is education and I believe such a step would bring much needed benefit to a sector that currently must agree to terms with companies on a case-by-case basis.

References

Prinsloo, Paul, 2020. Data frontiers and frontiers of power in (higher) education: a view of/from the Global South. Teaching in higher education, 25(4), pp.366–383.

Williamson, Ben, 2017. Digital Education Governance: political analytics, performativity and accountability. In: Big Data in Education, London: SAGE Publications.

Ozga, Jenny & Williamson, Ben, 2016. Trust in numbers? Digital Education Governance and the inspection process. European educational research journal EERJ, 15(1), pp.69–81.