Tag Archives: williamson

Block 3 – Reflection

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.


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.

Block 2 – Reflection

The last three weeks of visualisations I have tried to put emphasis on how best to show the data in a valuable way for teachers. My feelings through this section have been of frustration for several reasons:

  • What does the visualisation show?
  • What value does the data gathered hold?
  • Can any correlations be drawn from this data to research backed assumptions?
  • A piece of text might be better at explaining the outcome of the data collection than a dashboard

Williamson (2020) explained the frustration I felt for the first two reasons:

Data and metrics set limits on what can be known and what can be knowable. They define what is rendered visible or left invisible, thereby impacting on how certain practices, objects, behaviours and so on gain value, while others are not measured or valued.

As can be seen by this statement I faced a tough choice when choosing what data to capture and to display I thought “I’m going to miss huge swathes of data that could provide context for the data I’m collecting.” I may be able to show how many times I accessed my phone or laptop, but I cannot show that between 10am and 6pm on Wednesday the reason I did not access my laptop or phone was because the electricity was out because the visualisation doesn’t allow for that. If someone has limited access to broadband for example how can that data be gathered and shown.

In ‘The Platform Society’ (van Dijck 2018) it is stated that “critics draw attention to the fact that none of the presumed benefits cited by platforms have been proven empirically.” If the benefits have yet to be proven by some form of study, the platforms and proponents of online education can make any claim they wish. This is how unregulated areas of the economy function such as the vitamin industry making claims such as a vitamin can stop a virus without any evidence or how certain treatments in the cosmetic sector can make claims like ‘this will reverse ageing.’ If we place education as one of the pillars of society, we should possibly look at some level of regulation to stop wild claims being made.

And finally, after doing six visualisations I struggle to accept that dashboards are the best way to get across information. Within every visualisation I am making personal choices about what colours and styles to utilise and this is very similar to how dashboards and the underlying software is designed. This should not be the case it should be targeted information with as mentioned above some evidence behind what it is showing. With these issues and limitations of dashboards I would believe for the moment they should not be used. I would look at some form of text recap as much information and specifically individual student details cannot be expressed in a dashboard. There is a lot of work around machine learning and text so it would still be possible to provide a version similar to a dashboard but more granular.


Williamson, Ben, Bayne, Sian & Shay, Suellen, 2020. The datafication of teaching in Higher Education: critical issues and perspectives. Teaching in higher education, 25(4), pp.351–365.

van Dijck, José Poell, Thomas & de Waal, Martijn, 2018. The Platform Society, Chapter 6 Oxford: Oxford University Press USA – OSO.

Block 1 – Reflection

This block gave me a view of data capture and the overwhelming feeling I took away from it was:

The data that is collected is not what should be collected

The learning management systems (LMS) that are being utilised today gather information on the interactions and time users (the systems are tracking everyone) spend with the system. The value of this information improving peoples learning is questionable since it is a limited set of variables to do with interaction with the system. As discussed by Bulger (2016) most if not all current education systems are responsive, meaning that the system needs a ‘cause’ to then ‘react’. A teacher in most cases would be far more adaptive and be able to adapt to students prior to an issue arising.

Over the course of the three visualisations, I have tracked personal details such as how much interaction I had with course materials, how much sleep and exercise I got and finally how often I would have food or drink or snacks. These items show the level of interaction, how effective that interaction might be due to cognitive capacity and the distraction of having a cup of tea instead of focusing on course material.

The above items in my view are more important to understand, as the social life of the person shows more about what that person might be able to achieve. For example, if I do not sleep well that has a greater effect on my learning than what a system can tell from me rewinding a video several times. One of the ways to allow the LMS to see such a situation would be through wearables, those wearables as discussed by Knox et al (2020) could also provide feedback to students that would optimise when to learn and rest.

Personalisation within education has the goal of a single teacher to a single student (Friesen 2020). One of the main questions coming out of personalisation is if that teacher were a machine that could pass the Turing test is that not equal to a human teacher? In Benjamin Bloom’s paper “The 2 sigma problem” (1984) it shows that having the one to one, master – student relationship is highly beneficial. The problem though at this moment is that the master in machine learning does not exist, the technology has that goal of reaching it one day but currently it is not the case. We currently have a system as discussed above that has a limited set of variables to work from and no context around those variables e.g. sleep. A human working in a one-on-one relationship with a student can determine many different variables and through dialogue can understand the mood of the student and on a day to day basis manage their studies based on this.

With personalisation there is always a risk that a system might create a feedback loop for students. If the system recommends a certain subject or the administrator wants more focus on certain subjects they could ensure that the system nudges people in a certain direction (Knox et al 2020). Once a student is on that path perhaps the system continually pushes that new direction and the student unbeknownst to them is being nudged down a path they would not have wanted if they had freedom to explore.

Why? What? Where? When? – Introduction

The why

Currently I am working in the video conferencing and telephony world, to be more precise I work as part of a team in Cisco (the people behind Webex). This team is specifically focused on getting customers to test early release software and physical products around the “collaboration” space, which is a marketing way of saying communications like phones, instant messaging, enterprise video conferencing, etc.

At the start of this pandemic, I thought about tools like Webex, Zoom, Teams, etc, being used by new people and I knew that it would not be welcomed (it’s an acquired taste). Then I started to see how it was impacting people doing education and it was at that time I began to look at possibly doing some type of study around education and how it is done remotely and if it is successful.

I will say from University pretty much until today people have always mentioned to me that I should look at teaching or training people. It is an area that has brought me great pleasure when training new people and helping people understand different topics. I have always had an idea that it would be something I would do but was not sure how I would get to that point and I think the time we are in and the interest to learn a new skill has got me here. And who knows maybe a change of job at the end of it.

Why this module

Data is such a big part of my job and of pretty much everything now and being able to understand this area of Big Data and where it has come from is something that I have been looking to understand. From my current view Big Data is a word that is thrown around like “Cloud” is for computers, it is just a marketing term to make certain people feel comfortable in conversation, it doesn’t mean anything, there is no substance to the term.

The Edward Snowden revelations about data are also something that has me curious about Big Data. The revelations essentially showed that everything we do is being tracked and I was always curious how they (NSA) could wade through such large amounts of data and make sense of them (turns out they are not but it doesn’t stop them from collecting it).

There have been many other topics over the years that have piqued my interest in data and the lengths that people will go to to defend their right to it. If you have time the story of Aaron Swartz and his fight against the academic research paper industry is eye opening (and infuriating) one.

The Interest

I am curious to know the true weight put behind this “data” in education. I have seen data manipulated or certain variables intentionally not collected so that the issues cannot be tracked, and I want to see how that is within education and if the same happens.

The ability to be able to make data have value is an area that I find myself attracted to, for example in Dear Data how the data has value because Giorgia and Stefanie spent time mulling over it and really extracting the good.

I think over the course of IDEL it was the data modules that I took more of an interest in over the time. I will say that on listening to the podcast about what the upcoming modules where I thought the opportunity to have Ben Williamson as a part of the experience and hear his view was one not to miss.

Really looking forward to this module and I have already enjoyed the readings and the initial two weeks, onwards!