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As technology continues to transform the skills that today’s students need to shape their future, many countries are responding by layering more content on top of their school curricula and timetables. Adding new subject material is an easy way for education systems to show that they are responding to emerging demands, but it is always harder to remove older material. As a result, teachers plough through a large amount of content, leaving students with a limited depth of understanding – one that is a mile wide and an inch deep.

In today’s technology-rich world, many schools have begun teaching coding, the language we use to instruct today’s computers. It’s a skill that is in high demand, and there are intriguing examples of schools across the world teaching it in ways that are relevant and engaging for students. But the risk is that we will again be teaching students today’s techniques to solve tomorrow’s problems; by the time today’s students graduate, these techniques might already be obsolete. We should instead focus on the computational thinking that underpins these techniques – and that students can use to shape the technologies of tomorrow.

When studying national mathematics curricula for the development of the PISA 2003 assessment, I often asked myself why curricula devoted so much attention to teaching things like trigonometry. The answer cannot be found in the internal structure of the mathematics discipline; most mathematicians told me that trigonometry wasn’t a foundation for mathematical thinking or reasoning, but rather a very specific application of mathematics. Trigonometry does not figure prominently in the most meaningful learning progressions for students in mathematics, nor in the way mathematics is used in the world today.

The answer lies in how mathematics was used generations ago by people measuring the size of their fields, or performing advanced calculations that have long since been digitised. As British mathematician and entrepreneur Conrad Wolfram told me, “Mathematics education has often confused these elements because the key mechanics of the moment lasted for hundreds of years in hand calculations. You couldn’t execute four-step problem solving unless you could do this hard, expensive step with lots of training. But now that’s been turned on its head by machinery that makes that step the easiest and cheapest, most of the time”.

How can we focus learning on the ‘essence’ of a subject rather than the ‘mechanics of the moment’?

Similarly, in the wake of the 2008 financial crisis, policy makers sought to strengthen financial education in school and requested that these skills be tested in PISA, as well. The assumption was that more financial education would translate into better student performance in financial literacy. But when the first results were published in 2014, they showed no relationship between students’ financial literacy and the amount of financial education to which they were exposed. The top performer in the PISA assessment of financial literacy was Shanghai, whose schools did not provide much financial education. Shanghai’s secret to success on the PISA assessment of financial literacy may have been that its schools cultivate deep conceptual understanding and complex reasoning in the foundations of the mathematics curriculum. Because students in Shanghai could think like mathematicians, and understand the meaning of concepts such as probability, change or risk, they had no difficulties transferring and applying their knowledge to unfamiliar financial contexts. In contrast, students in other countries who had been taught specific tips and tricks in financial literacy had difficulties when solving problems in a different context.

These examples pose a larger question: how can we strengthen a deep understanding of and engagement with the underlying concepts of digitalisation without being distracted by today’s digital tools? How can we focus learning on the “essence” of a subject rather than the “mechanics of the moment” – the computational thinking that underpins the concept of algorithms, rather than the specific methods of coding an algorithm itself? Coding can be a great means to achieve this, but there is a serious risk that it becomes the end, and that school systems will continue teaching it years after it is obsolete.

We need to think more systematically about what we want to achieve from the design of curricula, rather than continuing to add more “stuff” to what is already being taught. Twenty-first-century curricula need to be characterised by rigour (building what is being taught on a high level of cognitive demand); by focus (prioritising depth over breadth of content to achieve conceptual understanding); and by coherence (sequencing instruction based on a scientific understanding of learning progressions and human development). Curricula need to remain true to the disciplines, while aiming at interdisciplinary learning and building students’ capacity to analyse problems through multiple lenses.

Curricula need to balance knowledge of discipline content with knowledge about the underlying nature and principles of disciplines. To help students address unknown future problems, curricula also need to focus on areas with the highest transfer value – in other words, they need to prioritise knowledge, skills and attitudes that can be learned in one context and applied to others. And to bring teachers along with this idea, they need to be explicit about the theory of action for how this transfer occurs. They need to balance the cognitive, social and emotional aspects of learning, and help teachers make shared responsibility among students part of the learning process. They need to frame learning in relevant and realistic contexts, and help teachers use approaches that are thematic, problem-based, project-based and centred around co-creation with their colleagues and their students. These are the principles against which we should assess any proposals to teach coding.

To determine what tomorrow’s students should learn, we must assemble the best minds in a given country – leading experts in the field, but also those who understand how students learn, as well as those who have a good understanding of how knowledge and skills are used in the real world. Such knowledge sharing will allow us to more precisely determine and regularly re-examine which topics should be taught and in what sequence – without succumbing to the temptations of the moment.