Towards Datafication?
Does data know best? Photo by Franki Chamaki on Unsplash

Understanding the risks associated with EdTech and the drive towards datafication of education is important.

“Government gets AI in transport, in health sciences, in retail,” he says. “It does not get AI in education — it doesn’t get the potential, it doesn’t get the risks. What’s more, it doesn’t know that it doesn’t get it.” — Political commentator and historian Sir Anthony Seldon’s interview with the Financial Times

During the COVID-19 pandemic schools and universities around the world rapidly closed and moved to online learning. Big tech companies such as Google and Microsoft have updated their education packages and software to accommodate the surge in demand.

The pandemic inevitably created markets best suited to the technology industry which gave these companies an advantage in the education sector. As the months went on new habits and norms formed which created a collective consensus around the need to continue learning during the different stages of the virus whilst maintaining high educational standards. However, the aims of government departments and big tech have longer-term plans to interweave EdTech into the day to day lives of students, teachers and educational institutions. Interestingly, the global technology education market is to reach $341B by 2025 so, it is even more important to understand the motives and the risks of EdTech start-ups.

EdTech Start-ups

AI education start-ups such as Up Learn, Knowledgehook and others aim to improve grades for all students by offering personalisation and customisation of learning for each student. It does this by learning the patterns of when a student remembers or forgets a piece of information, akin to reinforcement learning. This allows for more data about student performance and behaviour to be collected. This in theory is meant to help teachers update teaching strategies and inform students of their strengths and weaknesses. However, there is a risk that data points can also be quantified into behavioural insights. For example, looking at the recruitment industry many employers have adopted gamified approaches to sieve out candidates during the early recruitment process.

For example, the candidate downloads an app such as Sky Rise City. Playing the tasks on this app then allows the company to track and collect 5000 data points on a candidate’s personality, usually based on the big 5 personality model such as openness and neuroticism. The app determines these traits through many variables such as speediness of clicks and time taken to bounce back from failure. The collection of personality traits is highly contested. One of the criticisms is not just about the collection but how it can be used and manipulated without the consent of individuals. Most notably, seen with the Cambridge Analytica scandal. Looking back to EdTech tools it shows the slippery slope on how behaviour data can be quantified and manipulated.

Datafication

Focusing now on data and quantification, a popular article by William, Eynon and Potter analyses how the pandemic spurred the biggest EdTech social experiment ever seen. The ‘experiment’ is a chance to create and collect vast amounts of student, teacher and parent data. The experience of the experimental subjects e.g. students will start to leave digital footprints in which data engineers clean up, ready for analysis for future policy. They argue that the experiment will redesign educational policy and culture and what education is and what it will look like beyond COVID-19.

For EdTech companies interested in the monetisation of data the virus is an opportunity for conducting and learning from mini-experiments, it is similar to how social media companies use A/B testing to increase user engagement.

One of the problems researchers and policymakers have is using data to determine causality. The pandemic has created a natural experiment. For example, since around March students were required to sit all their exams online. This allows enough data for comparisons to be made with previous years where exams took place in exam halls. This allows for analysis without the problem of selection bias. Supporters suggest that this is a great opportunity to prove the success of virtual education over in-person education.

What happens if the research shows that students performed better online? Do educationists have a responsibility to adopt EdTech tools and online learning? Is educational success only measured by grades?

These are some questions to ask over the coming years. If these stances are adopted this could have knock-on effects on many parts of the education sector such as libraries as well as jobs within schools such as teachers and cleaners being at risk of becoming redundant. The scientific infatuation of measuring big data to develop accurate models to push education to the online world is further pushed by investors and start-ups to exploit the pandemic as a social experiment. This could lead policymakers to consolidate the use of EdTech and spur the rest of the education sector on the road to datafication which could leave jobs in precarious conditions and change the culture and nature of learning and the process of secondary socialisation.

The education sector through marketisation as well as other means has been the sector most affected by datafication. As it promotes efficiency, competition and standardisation. However, at the same time risks have also emerged such as continuing the culture of surveillance, control, privacy, manipulation as well as new and repackaged inequalities.

The quantification of student’s digital footprints during and after the pandemic shows the long-term motivations to lock in as Collingridgedescribes EdTech tools into mainstream education but instead of disrupting inequality or improving student experience these tools may exacerbate existing inequalities, uproot and embed technology risks and leave jobs in precarious conditions. Therefore, if EdTech becomes locked in it may further disenfranchise groups of people from education, reinforce a specific type of learning and success and favour the interests of investors.

Thoughts

Can we go back once datafication of education expands and EdTech tools become locked in?

‘’Control may still be possible, but it has become very difficult, expensive and slow.’’- David Collingridge, The social control of technology, pg. 18

Collingridge’s quote from the 1980s is still significant and may give us some insight. It seems that change within the education sector more so now that it is governed by market forces may be political.

Who defines the problem within the education system as a problem? Is it the teachers, students, politicians or big tech?

Why might the decision process be slow when it comes to education policy? Do we rely too much on the future of EdTech to solves these problems?

It is undoubtedly true that technology has benefited and aided students before and during the pandemic. However, as technology continues to grow exponentially and becomes embedded in traditional sectors it is important to discuss critically the risks that we know of. For risks that we don’t know of, we could follow Ibo van de Poel’s advice of incremental steps which can help us become less ignorant and more critical around the allure of new technologies. Whilst at the same time instead of relying on numbers solely, we can incorporate views from the social sciences such as anthropology to evaluate how these risks affect our behaviour and the development of society.
['Maisha Chowdhury']
['Politics', 'Data', 'Technology', 'Education', 'Edtech']