Mathematics and statistics are not niche skills!

When I tell people that Grasple is an interactive, adaptive learning platform for mathematics and statistics in higher education, I often see a familiar reaction. They nod politely and mentally file it away under "specialist tools for specialist subjects." In the crowded ecosystem of learning management systems, assessment platforms and AI content generators, a dedicated maths and stats platform might indeed seem like a niche product.

But here is the uncomfortable truth that keeps me awake at night: whilst our platform might occupy a specific corner of the EdTech market, the skills we support are anything but niche.

The omnipresence of quantitative thinking

Open any newspaper or scroll through your social media feed today. You will encounter numbers everywhere. Climate scientists present data on rising temperatures and carbon emissions. Politicians debate migration statistics. Technology companies explain how their algorithms work. Social media platforms reveal trending mechanisms. Every single one of these discussions requires us, as citizens, to engage with data, interpret statistics and think critically about numbers.

This is not just about reading numbers. It is about understanding what they mean, questioning how they were collected, recognising when they are being misused and making informed decisions based on quantitative evidence. These skills have become as fundamental to modern citizenship as literacy itself.

Consider the reach of statistical thinking across disciplines. Social scientists rely on statistics to understand human behaviour patterns. Legal professionals use statistical evidence in courtrooms, sometimes with devastating consequences when applied incorrectly. Business professionals interpret market data to make strategic decisions. Health professionals evaluate research methodology to provide evidence based care. Even creative professionals analyse audience data to measure impact and understand engagement.

The quantitative thread runs through every field of study, every profession, every meaningful conversation about the world we live in.

The confidence crisis

Yet despite this universal need, we have collectively accepted a bizarre social norm. It remains perfectly acceptable, even fashionable in some circles, to declare "I am not a maths person" or "I cannot do numbers." We would never accept this attitude towards reading or writing, but somehow, mathematics anxiety has become normalised.

This is not about ability. Research consistently shows that mathematical thinking is not an innate talent that some people possess and others lack. The difference lies in confidence, in early experiences, in the belief that "I can understand this if I persist."

When students hit a wall with numbers, they often avoid entire subjects that genuinely interest them. A student passionate about sustainability may struggle with carbon footprint calculations. Someone fascinated by artificial intelligence may falter when confronted with the underlying mathematical concepts. A future entrepreneur may abandon their business aspirations when faced with market data analysis.

The tragedy is not that students grapple with quantitative concepts. Struggle is part of learning. The tragedy is that they interpret this struggle as evidence of fundamental inability rather than as a normal part of the learning process.

Beyond career preparation

Here is what strikes me most forcefully about mathematics and statistics education. It is not really about the numbers at all.

When a student works through a challenging problem, tries different approaches, experiences frustration, persists anyway and finally achieves understanding, they are learning something far more valuable than the specific mathematical concept. They are learning resilience. They are learning that difficult things can be mastered. They are developing the habit of persistence in the face of uncertainty.

These are life skills, not merely career skills.

Every time someone interprets a graph, evaluates a claim based on evidence, or solves a problem that does not have an obvious solution, they are engaging in mathematical thinking. This kind of thinking shapes how we deal with uncertainty, how we approach unfamiliar challenges and how we grow as learners throughout our lives.

Even students at technical universities, surrounded by future engineers and scientists, regularly ask "Why do I need to do all this maths?" The answer is never really about the formulas themselves. It is about developing the capacity to think critically, to solve problems creatively and to believe "I can manage difficult things."

The embedding imperative

So whilst Grasple may be a niche tool in the EdTech marketplace, the skills we support must not be treated as niche in education. Mathematics, statistics and broader STEM thinking need to be embedded across the curriculum because they are not separate from other disciplines. They are foundational to all of them.

This means more than just requiring students to take a statistics course in their first year and then considering the job done. It means weaving quantitative thinking throughout their educational journey. It means connecting numerical concepts to real applications in their chosen fields. It means providing support at the point of need, when students encounter data in a sustainability module or calculations in a circular economy course or research methods in a social science project.

Think about how we have already solved similar challenges with other technologies. When you read a digital text in a different language, you can instantly look up words you do not understand. The translation tool is right there, embedded at the point of need. There is no shame in using it, no barrier to access, just seamless support exactly when you need it.

We need the same approach for STEM skills. Not separate remedial courses that students must complete before they can engage with their chosen subjects. Not additional modules that feel disconnected from their real interests. But a hybrid approach: embedded support that appears exactly when a student encounters a quantitative concept they struggle with, combined with clear visibility of how mathematical concepts build upon each other. When students can see which prerequisite skills underpin what they are learning, when they understand the connected network of foundational knowledge, they gain more than quick fixes. They develop genuine mathematical competence in the context of subjects they actually care about

When a sustainability student hits a calculation about carbon emissions, they should be able to access targeted support on that specific mathematical concept, then immediately return to their environmental work with new confidence. When a social science student needs to interpret statistical findings, the help should be there at that precise moment, not relegated to a separate maths class they took months earlier.

This is what true embedding looks like. Support that is contextual, timely and directly connected to the learner's goals.

It means, fundamentally, treating numerical literacy with the same importance we afford to written literacy.

Building confidence, not just competence

The goal is not to turn every student into a mathematician or statistician. The goal is to ensure that no student avoids a subject they love because they lack confidence with numbers. The goal is to develop citizens who can engage critically with the data driven world they inhabit. The goal is to prepare learners who approach challenges with curiosity rather than fear.

This requires us to rethink how we position STEM skills in education. Not as specialist knowledge for specialist careers, but as fundamental capabilities that unlock access to virtually every field of study and every meaningful way of engaging with our complex world.

Mathematics and statistics are not niche. They are universal. And our educational systems need to reflect this reality.