Something doesn’t add up — a response to the government ‘maths-to-18’ initiative

EVR
Professor Rose Luckin’s EDUCATE
6 min readApr 21, 2023
Photo by Michael Dziedzic on Unsplash

On Monday 17th April 2023, the UK’s Secretary of State for Education, Gillian Keegan, was interviewed on Radio 4 about the government’s initiative to have every schoolchild carry maths through to the age of 18. Of particular note in her response was the justification that the next generation would be “operating in an ever increasingly digitised world where AI is actually going to be part of their day job. We really do need to make sure they understand not just maths at every level, but understand statistics, how to analyse data”.

Although the intention behind the initiative could be seen as noble — equipping learners with highly marketable skills for future work — Keegan’s two lines perfectly illustrate the concerns the initiative has raised:

  1. Not everybody wants to study maths up to A-Level
  2. Not everybody needs to, even if they do want to be part of that ‘increasingly digitised world’

Last year, for the EVR Byte-Sized EdTech Library, I adapted a research paper by my colleagues Orli Weiser and Carmel Kent, called ’65 Competencies: Which ones should your data analytics experts have?’

Image from the EVR Byte-Sized EdTech Research piece: ‘Competencies in Data Analysis, what to look for in recruitment’, adapted from the full research paper available on the EVR website

The research asked expert data analysts what their opinions were on the most important competencies for the field, and how to identify and measure them for candidates in the role. Literature reviews had revealed discrepancies between ideas of the required skills and capabilities of the role, and that policymakers and educators often therefore looked to other sets of skills to use as benchmarks, such as ‘21st-century skills’.

Reflecting on the government’s initiative, it seems incredibly important to me to consider that when the experts in the research were asked, there was over 90% consensus that competencies such as ‘a drive for continuous learning’, ‘problem-solving’, and ‘reasoning and argumentation’ were as key to the role of data analyst as that of the obvious competency of ‘analysis’.

Image from the EVR Byte-Sized EdTech Research piece: ‘Competencies in Data Analysis, what to look for in recruitment’, adapted from the full research paper available on the EVR website

24 further competencies received over 75% consensus from experts, and perhaps most interestingly, there was no consensus among anyone that ‘ethics’ was one of the most important competencies to succeed in assignments.

From the research: “while the ‘hard’ technical skills associated with programming remain a prerequisite for new hires, the industry also wants workers who can demonstrate a range of so-called ‘soft’ skills”.

In other words, the experts stress there is much more to the role of data analyst than what sits in the job title.

Image from the EVR Byte-Sized EdTech Research piece: ‘Competencies in Data Analysis, what to look for in recruitment’, adapted from the full research paper available on the EVR website

Perhaps at this point, however, you think I’m being a bit dishonest, strawmanning Keegan’s comments because I might have some kind of allergy to maths. I’d like to tell you about my experiences with school and work.

When I was 16, I tried to take physics as one of my A-Levels, despite the fact I had done my GCSE maths in the class for the lowest scoring pupils. I love physics: light, radiation, mass, temperature, space. I write and draw science fiction comics, and I love watching Frank Summers’ STSI Hubble lecture series every month. But to this day I can’t do maths, not even simple maths, and after one month I had to drop the class because the stress (combined with one particularly abusive teacher who delighted in my in-class humiliation) put my stomach in knots. I was told that as much as I wanted to learn, I wouldn’t be allowed to sit just half a class, so I had to go and do something else.

It turned out that there was nothing else. I’m still sceptical that if I’d been seen as one of the brighter children in my year they would have forced me to take another subject rather than just letting me run wild and free, so instead I went to the library and taught myself to write. I spent a whole year with one less subject than everyone else, and crafted a skill of my own choice, under my own steam. It’s no exaggeration to say that my skill in writing has got me every job of the last 20 years, and kept me in quite a few too.

Photo by 🇸🇮 Janko Ferlič on Unsplash

I am EVR’s Creative Producer. I don’t code, apply algorithms, or do long division in the team, but I perform the roles of operations manager, illustrator and graphic designer, pitch advisor, scientific translator, quality assurer, copywriter and editor, web, socials and communications manager, podcaster, video editor, instructor, host and presenter, and I water every single one of the office pot plants. I work with statisticians and data analysts in an AI company but I don’t need to know how to do their jobs to do mine and bring value to our team.

An understanding of maths ‘at every level’ is a envious skill to have, if it’s one you wish to cultivate, but using the threat of AI and an ‘increasingly digitised world’ as justification for its pursuit, to the exclusion of some other essential subject, is short-sighted.

AI is something of which everyone should have some knowledge, but it’s not required that the population as a whole understand its technical wizardry. Although AI is good at pattern matching and classification, automating and replication of repetitive tasks, the processing and storage of large amounts of data, and the reduction of complex phenomena for human cognition, humans are good at a lot more things besides!

Humans can think about what we know, what we don’t know, our ability to understand what knowledge or evidence is, and how we make decisions about what we should believe and not. We have an understanding of multiple different environments, people, and tasks that we work with, that we often move seamlessly between. How would an AI travel across the world, work out where to go, what to say, what to ask, and to whom, in a foreign country, in a foreign language? It’s a combination of intelligences, human and artificial, that will be required, and we really don’t want to minimise what other skills, competencies, and intelligences we possess now, that machines and those industries that work with them, might need in the future. The experts agree there’s a lot more to the job than just what’s in the title.

Part of the interview with Gillian Keegan was around the attempt to recruit maths teaching expertise inschools, and how difficult it had been for the government thus far.

For me, it wouldn’t matter if you had all the maths teachers or all the maths apps in schools that you needed, the premise of the argument is false. Not everyone wants to take maths up to 18, and A-Level maths is still not required of everybody working, even in the industries and roles cited as justification for the initiative in the first place.

I work in AI. I don’t code, apply algorithms, or do long division. For my boss, it’s enough that I just write about it.

Thanks for reading,

Rowland Wells, Creative Producer, EVR

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EVR
Professor Rose Luckin’s EDUCATE

EVR is an AI consultancy for education and training institutions