Conrad Wolfram on Computational Thinking

You’re listening to the Getting Smart podcast where we unpack what is new and innovative in education. I’m your host Jessica and today we’re bringing you an episode on computational thinking. Conrad Wolfram directs global strategy for Wolfram research, a leader in computational resources. For the last decade Wolfram has been encouraging educators to teach math as if computers existed. He wants teachers and policymakers to stop fixating on calculations

like long division and factoring polynomial equations and start focusing on computational thinking. He lives in Oxford, England, so you’ll notice that he adds an S to math. His new book, The Maths Fix, is the foundation for a revolution in education. Let’s listen in as he talks to Tom. Conrad Wolfram, welcome to the Getting Smart podcast.

Great to be here. It is so exciting to have you on. I stayed up late finishing your new book called The Maths Fix, an education blueprint for the AI age. Thanks for the book and your contributions to mathematics. That’s very nice to hear because of course it’s early days, so it’s nice to hear fresh

readers and how it got on. If I manage to keep you up for that reason, I guess I’m pretty grateful for that. Your family does math. Maybe you could tell us a little bit about the family business to give us an indication that you know this topic pretty well. My brother founded just over 32 years ago, 20 or at least the first edition of our technology

came out, Mathematica in 1988. In a sense, that was a way to do mathematics on computer. Then I joined up as I left college to start the European part of that. I read math and physics in Cambridge in the UK. Ever since then, we’ve been trying to figure out how to get computers to do better at helping people in the world use this power of computation, which we think is such a great way of enabling in a sense better decisions in the world.

That’s sort of the background which has put me right in the centre of watching how millions of folks in the real world actually use mathematics and all its ways and how it’s really moved us forward in the last 30 plus years. It has kind of brought me to the point of thinking how the education might work around that. Conrad, what is the Wolfram language?

Wolfram language is a way to write down technical ideas for a computer. At one level, it’s a programming language. Like other programming languages, you might say Python is a popular one at the moment. But what we’ve tried to do with the Wolfram language is make it a couple of other things as well. We’ve tried to make it high enough level so that in a sense as a human, you might want to write down what you’re saying in that way. In traditional

mathematics, you write down funny symbols to represent things that have been born over several hundred years. I see the Wolfram language as a way to write down a more general set of in a sense what you’d like to describe computationally. It’s a programming language in a sense that executes things on a computer. We think we do that very nicely at a high level and with many different… We’ve also got a huge number of algorithms built in, ready to

roll. We think that’s all very nice, of course. But it’s also this way of describing precisely, abstractly, a world of computation as well. I think your brother and I are both about the same age. We both experienced in our 40-year careers the information age when we began to incorporate computing technology into every aspect of life. It does feel like we’re a couple years into a new age, the one that comes after

the information age, where we’re beginning to incorporate machine learning and big data into every aspect of life. Maybe you could reflect both on how human beings have begun to incorporate computing technology. And then secondly, what’s new about this new era that we’re in today, at least specific to computation? Yeah, these are big questions. Let me maybe take the second one first more. I think we’re into what

term in my book quite a lot, the AI age, or some people call the Fourth Industrial Revolution. I suppose, in my mind, what marks it out is we’ve got a new era where we’re really sharing intelligence with machines. One can argue about what intelligence really is. But I feel like previous industrial revolutions were really about sort of brawn not brain, as in you’re physically getting a machine to do something for you to a large extent.

Now we’re into what appears to be this quintessentially human characteristic of intelligence. And we’ve got machines somehow sharing that with us. And I think what’s going to mark out the era we’re coming into is kind of who’s on top doing that? Is it the human or the machine? And how do we best manage that? And of course, how do we educate people set up for that hybrid world of intelligence between the machine and the human? And in a sense,

I suppose one of the defining issues there is what is the, in a sense, what’s the value add that the human can now give? And what should we just leave to the machine? Now in a sense, that’s a question that’s been had in every industrial revolution. And it’s, but it sort of, it seems more intertwined, given the nature of this one being around sort of intelligence. You know, another way to think about it, I sometimes term that used the phrase

computational knowledge economy, I think I started using a few years ago, by which I mean, you know, I guess, Druker came up, I think it was in the 50s with knowledge economy, I’ve never totally understood the definition, but my way of thinking about that is, it’s where a big fraction of a developed economy is to do with knowledge rather than to do with physical action. And I feel we’re now into this new age where it’s kind of like it’s a mixture

of what you can compute from the knowledge. It’s not really just knowing that it’s not knowing the raw information, because that’s already being handled very well by our computing machinery as well. So that’s a sort of another take on, on that side of it. And I suppose the real change, another change to look at is, it’s kind of like computing has been an add-on for many things as it’s been growing into our societies. And now it’s sort of center stage.

And we see this very, very directly in Wolfram, because when we started, as I said, 32 years ago with our first release, it was like we were in the R&D department, so we were in the universities. And it’s like, it’s very interesting, you’ve got Mathematica, and that’s a side issue for us in some organization, particularly in large companies, for example. Now what we see is that something we’re very connected with data science, which is our biggest single market,

what we’re, you know, in a sense, that’s something that is center stage. Most, most boards are discussing data science in their organization. It’s very, very central to our lives. And so I think in a sense, that’s a change over our careers that we’ve seen this sort of center stage of computation in a way where it was important before, but it wasn’t center stage, wasn’t an everyday thing for everybody. And just to add, our current pandemic, we’ve seen this absolutely.

I mean, we’ve seen the discussion, the public discourse about, you know, squashing curves and transmission rates and all sorts of things. There are many things, I think, wrong with that discourse, by the way, which I hope we can help to fix with improved education. But those things are, again, that’s a center of stage that you wouldn’t have seen, I think, even a decade or two ago in public discourse. You recently published the book that I mentioned at the outset, The Maths Fix,

an education blueprint for the AI age. It strikes me that you make two core suppositions there. I think they’re summarized in this statement that you propose a fundamentally new core computational subject at school with a change to assumption that computers exist and students should learn to use them. So it strikes me that there’s two ideas there. One, the importance of computational thinking across the curriculum. And two, that we have these powerful supercomputers in

our pockets now, and that we should begin to use them rather than focus all of our time in school on learning to hand calculate. Are those the two big ideas? Yes. The only slight hesitation was whether I want to include any more. But yes, I think that is. In the end, we’ve got machines that have upended what we can do with computation beyond any previous imagination. It’s hard to think through history of something that’s become

so unbelievably mechanized. By which I mean, you could have taken a mathematician who might have spent their whole life trying to compute things that now you can compute in a second or something on a machine. So we have this complete upending caused by machinery. But I see that as an incredibly positive, powerful way to utilize this process of decision making and problem solving, the mathematical process. And for reasons that I explore a lot in the book, and

in a sense, it’s hard to understand if you just look at this very dispassionately, but then it’s easier to understand when you see all the pieces that haven’t quite come together. We haven’t managed to replicate this real world in which fundamentally, computers do the calculating on almost everywhere and have massively escalated what’s done. And in education, we are in an era where we believe that the human is the primary calculator. And that misassumption is very, as I explore in the book,

in my view, highly detrimental to much of our population and what they can do, equity, many other issues. It’s how many people can engage in this decision making using computation, both at a high level and societally, or what I call computational literacy. And so yeah, my main focus is thesis in a sense is to say, if we assumed computers do the calculating in the way that, in education, in the way that we do in the real world, we can massively improve

one of the most key areas of education. And it’s particularly key now we’re entering this AI age. And that’s in a sense also a blueprint for how some other subjects need to think about what to do in this new hybrid computer human era. These are two big and important points. There’s some of our listeners that are going to argue that there’s still some benefit to hand calculation. I think we’ve both dismissed that notion largely. But this point that everyone in every walk of life,

in every job, needs to learn to think computationally, that seems now to be irrefutably important. I do, I love the way that you think that you’ve defined and illustrated computational thinking as learning to define questions, to abstract them to computable forms, to compute answers, and then to interpret results and that the learning process should really be cycles of this computational thinking. Did I get that about right? Yes. I mean, at one level, I want to explain,

a computational thinking is an incredibly rich, powerful thing we have to learn. And we should learn much more of and much better. And that can help us in our lives. And then, you know, one can go on learning that forever. At another level, it’s a full step process, more or less, as you correctly mentioned, which is, you know, you’re defining questions, you’re trying to abstract them to this really powerful language of mathematics or computation. And you’re doing that in step two

because by putting them in an abstract form, you can take many apparently disparate questions and apply and put them in this form that we have ways to compute answers from, which we’ve developed over hundreds of years and we now have very good machines to do. So you’re in step two, which is this abstraction, then you’re moving to, in a sense, what you’ve done is set up the question that you want to ask, but in an abstract form. Then step three,

you’re taking that question and moving it to an answer. So traditionally, you might be solving an equation, you might say, you know, the question is x minus two equals four, for example. And then, in step three, you’re taking that and you’re computing the answer by the end of step three, you’re saying, well, actually x equals six, if I’ve got that right. And then the fourth step is, you’re taking an abstract answer and saying, well, did six x equals six? Does that now represent

something that we asked in the, you know, in the defined step? Does that, let’s turn that back to the real world question we asked. Does that seem like a reasonable answer? Do we have to go through these four steps again? And in fact, we’ve sort of thought of the imagery of a helix, you’re going out these four steps up around this sort of helix, until you get to finally an answer that you think is sufficiently good, but you say, fine, that’s my answer. And that’s how I’ve applied

the computational thinking process. What I think is, I mean, just to look at the early part of your question, you know, this is very much, I think, for the future in society akin to how literacy became a society-wide imperative. And now it seems obvious that everyone should learn to read and write. I mean, in any developed society, it just seems like that’s, I mean, how could you ever suggest otherwise? But that is a, you know, it’s a relatively new concept. I think it dates to the

sort of early, early to mid 19th century. And there were all sorts of things said then, like, you know, well, most, more or less, most of the population were too dumb to learn how to do this. I’m not quite sure in those words, but pretty much that was the focus of it. It was just impossible. You know, most people could never get to grips with it. It was just for a few high-faluting people. And it was also many arguments about how it wasn’t really necessary, about how being this way.

The fact is, I think that has been a shining success in education. If you, you know, the idea of universal literacy and that ability has massively improved a lot of humankind, I think. And I feel like computational literacy is, you know, is a similar kind of issue to that. And it feels at the moment, like really, you can’t get most people up on this to the sort of issues that I’m talking about. But I think you can. Yeah. So I appreciate that, that observation. And your book does a great

job making the case here that it’s used in everyday living. It’s used in technology focused jobs. It’s important for what you call logical mind training that’s useful in all aspects of life. Page 32 in your book is a great chart that shows how computational literacy is increasingly important in every field. I think that’s lesson number one from your book that every educator should read. The math speaks to fully understand the importance of computational

literacy in every field. I want to turn to the flip side of this and talk about why the focus on calculation, hand calculation, has become so dangerous. On page 87, your book says the mainstream subject has ended up being highly procedural and un-conceptual, while simultaneously being impractical for direct application. That’s actually a great summary of what’s happened to mathematics education. Say more about why that focus on calculation

and the assessment of the ability to calculate has become so damaging. I think it’s pretty, it’s pretty much a perfect storm. Let me see. It’s quite a complicated, there are various ways into this. So at one level, assessments and the quantification of assessments have become the linchpin for much of secondary education. There are good reasons as well as bad reasons for this. There are reasons why this has happened that good people have figured out good things

here. It’s just that the combination of things hasn’t quite so. We’ve got assessments that everybody’s striving towards that rank you in a number with a number tell you how you’ve done. We’ve also got the knowledge that mathematics appears to be very important in the world, for all the reasons we’ve just talked about and people have got that message. Then we’ve got also the fact that mathematics appears to be very right and wrong.

So if you put those together, you can see why how you score in maths in an exam and an assessment today seems like a critical issue for your life. If you do badly in it, there’s a problem. Basically, the suggestions, if you do badly in it, somehow you’re not as good as somebody who does good in it, does well in it. I think that’s how we’ve had this mathematics as it currently is so at the centre of this. Now, what’s wrong with that? The problem is that the mathematics we’re

teaching in schools that the subject, in my view, as I say, is around 80% discrepant from the real world subject that I think we actually need. That discrepancy is largely to the fact that we’re getting students to learn how to calculate, rather than apply this four-step process we talked about using a computer to actually much harder problems, just to point out. I mean, more conceptual, harder, more intellectual problems actually than we’re doing at the moment, just to make that clear.

So we’re effectively testing them on a proxy to what they may actually do. And we’re then putting huge emphasis on that test and the result of it. Now, some people may do well at the proxy anyway, and they may be great computational thinkers, they may do well at the proxy, and in a sense, to some extent, we’re all well and good. I think they could have spent some years learning stuff that was more useful to them. They’d probably get

through the system and be able to carry on okay. Some people, they’re just not that good at the proxy, and they just don’t have to. Actually, it doesn’t necessarily mean they’re not very good computational thinkers, or computational thinking I’m talking about. And so we’re cutting those people out. We’re saying, sorry, you can’t go, certainly you can’t do any technical subjects. And actually, you might not be able to do any subjects at a good university, for example,

that have not much to do with this at all. And think of all the anxiety that’s caused by people failing at maths exams and having to retake them. And then you also pile on the fact that it’s a very abstract subject in its current form. I often cite the question, when was the last time you solved the quadratic equation? I often ask government ministers and policy makers this question, when was the last time you solved the quadratic equation? If they’re very quick on the

mark, they off the mark, they say, oh, to help my kids at school. But realistically, none of us really solved quadratic equations by hand anymore. And whether you can do that doesn’t need to be a sort of zero to 60 speed test that you’d have in your car. It doesn’t necessarily mean anything about your intellect in any other way. And so it’s very damaging this, because I think we’re cutting a lot of people out. I think we’re particularly doing badly with people in lower

socioeconomic groups who often what marks out characteristics in this group, I think is low confidence, something where you need to see it attached to your real life quicker, not necessarily because there’s anything wrong with their abstract thinking or abilities, just because necessarily they don’t have the confidence, the background to push through that as people who are sort of from a higher socioeconomic group might be able to naturally.

And so that cuts all of those people out from doing this as its subject, even in its own right, but more importantly, from being able to apply this to real life. And so we’ve kind of ended up with this proxy that’s sort of all empowering, all discriminating so to speak on who can go further. And one of the things I point to in the book is there is an example of where this happened before, perhaps not so familiar in the US, but certainly in British schools and many European

schools in the 1950s, Latin and classics assumed a similar position. In order to enter good university in the UK, you had to have studied Latin and be good at it all the way through until you were 18 years old. And now it seems sort of crazy. I mean, if you don’t happen to be good at learning your Latin words, or you know, knowing how to decline a verb, it doesn’t seem like it has much to do with whether you’re going to be a great biologist. And that’s correct, it doesn’t.

But yet that was a thing that you had to do. That was a milestone you had to get passed in order to get through. And that is why I think this is so dangerous in the world. And it’s particularly dangerous because I think that much of the discourse, political discourse, is now, it has a computational element to it. And so if we cut a large fraction of the population out, you can see we’re cutting them out from much of the decision making. And that has other

consequences. Hey listeners, Jessica here. I wanted to just take a quick break from today’s episode to let you know that Getting Smart offers advertising opportunities on our podcast and on our website. Do you need to get the word out about a new campaign or initiative? Want more school leaders and teachers to plan for the new school year with your edtech product in mind? If you’re interested in sponsorship or want to learn more about ad placements, just shoot me an email at

info at gettingsmart.com. All right, let’s get back to the podcast. I really appreciate that argument. And in your book, you make a number of super important points about this. Historically, I took a lot of math in college because I was an engineer. I didn’t really do any interesting problems until I was a senior. When I had had about six math classes, I found the same about engineering. I didn’t really get into any applied work until I was

well on into my studies. And that was because you delay things until you’ve been able to do advanced calculation. And you point out on page 132 that it’s, it’s crazy that we do that. We should actually reorder by the complexity of the problem, not the calculation. And you made some great points that 10 year olds can use some of the concepts of linear algebra to conceptualize a problem. And that’s not at all a challenge because you can do those calculations by computer. But

we should, we should be inviting young people into increasingly complicated problems and not getting those by how difficult it is to do the hand calculations associated with them. Let me maybe sort of broaden on that as well. I think, yeah, I mean, this reordering is really exciting. And the questions I ask is why don’t we teach machine learning to, you know, in primary schools in, you know, to pretty low age group kids? Actually, machine learning is the thing to

understand how you use it. It’s pretty simple. Right. It’s almost how they, how they learn stuff, how we all learn stuff. Building a new neural network, totally different thing. But we have to separate and we have to be very careful to separate the building of new things, new, new techniques from the use of them. Once you have a computer actually doing the calculation, you can separate this one. There’s a layer of automation. So there’s no reason not to introduce

the, you know, things that, you know, introduce topics that may require complicated computation under the surface, but maybe conceptually quite simple to understand. And I would also put calculus, the use of calculus, actually quite… Just talking about rates of change. Yeah, rates of change, areas under curves. You know, how much I’ve got a curved desk in front of me here. I’m not sure what function it was made with, but you know, how much material do I need to make

this desk? I mean, it’s not hard for a 10-year-old to understand the problem. It’s nasty to do the actual integral because, you know, that leads to the manipulation, but we don’t need to do that anymore. And so I think what we, we’re into an era which is very exciting where we can do a lot of complex problems early on, which appear to relate to, which would do relate to the real world much more closely than what we’ve been doing. So we can start from real-world actual problems that

might be of interest to the student, and then we can work something out. And as opposed to starting from something very abstract, and if you’re very lucky after you’ve done your equation solving, you might be able to have it hooked up, you know, to the real world somehow, by which point you’ve already lost, you know, three-quarters of the students. All right, I want to make a couple of, this is sort of a lightning round of topics to help make this tangible for people. On page

90 of your book, you take on the question of time stables, and if it’s still useful to memorize time stables, you have a common sense answer to that question. I think it’s still somewhat useful, but I wouldn’t put them on any pedestal. I think one of the things, I don’t say we shouldn’t learn anything by hand. I have two discriminators for that. One is, is it useful today? Is it practically useful? And I use timetables for estimating things in my head.

I don’t think there anything very exciting, but that’s what I do. And so I find that, you know, somewhat useful up to about 10 times. Another reason potentially for learning is by hand, is because it really does conceptually empower something further ahead. We’ve got to be extremely careful of that one. I think there’s a lot of misrepresentation where it’s like it claims that that’s the case, but really that isn’t empowering something further forward. You could just learn

the thing further forward by itself. So what about fractions, proportionality and and manipulating fractions? How much that’s interesting. So proportionality, I said, you know, is a core issue just by the way, just back to multiplication. The idea of multiplication, very important. The idea of proportionality, you know, half of this. Now, then you start asking questions about the details like, okay, when was the last time I added a half and a third in

real life? It’s pretty rare. I’ve done anything like that. On the other hand, I might ask, you know, given that this wall is 7.3, you know, I’m in UK, 7.3 meters, meters long, what’s, you know, what’s a third of the way along that. So multiplying a decimal by a fraction, I think somewhat useful, but I could do that on a computer. The main thing I really need to understand is how to set the problem up. So the setup of the problem is typically much more important than the

computation. And by the way, on the times tables, even though I think it’s somewhat useful, it’s much more critical people understand when they would use a times table than how to compute it. Yeah, I this is a great set of points because I think in the US, we start to trip kids up in the intermediate grades, fifth grade, sixth grade, with extensive fraction manipulation before they move into algebra. I think this is the beginning of computation taken to a bizarre

extreme. And around the same time we teach long division, which I think you don’t have much patience for. Well, not only not much patience, I’ve never actually learned how to do it, even though I have a degree in mathematics from Cambridge University, not their formal long division. Right. It’s ridiculous. That’s a key thing where you really don’t see people doing that anymore. No, it’s ridiculous that we torture kids with long division. And as we move into algebra, what about

factoring polynomials? How much of that is of use? I think it’s it’s pretty limited. I mean, I think that again, you’ve got to what one thing we found in thinking about is you’ve got to pick through each thing very, very carefully. And, and I have a nice anecdote I enjoy when I was with a real cabbie in London, a taxi driver who was asking me, what’s the role of algebra then? It’s a very good question. I think that the setup of our

algebra is a tool set, in my view, alongside machine learning on site, various data science tool sets, etc, etc, etc. We have many, many tool sets. And by the way, we only normally deal with a very small fraction education. That’s another problem which we need to enlarge. Algebra is a tool set. You need to know how to use the tool set in sense of when to use it, how you’d set up a problem, abstract to it, what sort of problems are relevant when it goes wrong, what tends to trip it

up. But once you set up the problem, and you have ways to verify it, you leave the computer to do it. So actually the details of knowing what factorization is might be useful in solving a problem. Actually, the details of exactly what you do to action it, typically not. Right. Very important point that algebraic reasoning, algebraic thinking, the ability to break a problem into component parts to understand what you know and what you don’t know, to understand

which variables impact other variables and how those are critical. And we use those all day, every day, in every walk of life. Right? Absolutely. And we’re not doing much of that in high school math at the moment. Because what we’re doing is we’re teaching kids order of procedures. Right. And we’re teaching them things that we can assess quickly on a quiz. That’s right. And one of the things I, you know, there’s a messiness in real life.

Right. I mean, one of the things I think, mathematicians often really don’t like me for saying this, but I say it anyway, which is that before computers, maths had a fairly limited scope of what it could help you with. I mean, it was important but limited. You know, there’s certainly hard sciences like physics, it was very successful for a long time. It was successful for accountancy. It wasn’t very successful for things like biology, because the start of the

world was too messy to deal with in what you could hand the set of tool sets and possible things you could work out when you were hand calculating. So computers have liberated mathematics from that relatively narrow scope. And in fact, that liberation in the real world is exactly what’s driven people’s wish to have so many more students educated in its scope. But unfortunately, we then decided in education, we’re going to limit down to more or less what we can hand calculate, which

basically strips out almost all the context of the real world that’s driven the use of mathematics in the last 50 or so years. So it’s a funny piece of logic there that we’ve ended up with. Conrad, I was struck at many points during your book that this is an exciting way to think about education. One is introducing young people to increasingly complex problems and creating the muscle memory of how to apply these steps of computational thinking. But it struck me that

it’s a more challenging way to teach than the rote memorization and rule application that we’ve thought about mathematics. So what’s your take on that? Is this a more challenging way to teach? I think there are new challenges, yes. And I suppose it probably is more challenging as well as new. I mean, in the end, we’ve got to step humans up to a higher level of ability to reason and think because we’ve now got machines that do the lower level. In order to do that, it’s going to be

somewhat harder in education because we’re trying to access a higher level of sophistication and that’s going to need more sophistication in terms of what we do at school. However, I mean, several things I’d say. The first thing to say, by the way, is I think teachers, humans, are very important in this process. It’s very important not to muddle up. Computers being used to automate pedagogy in some way. And there are ways, by the way, computers can really help

in that. But that doesn’t throw out the teacher in any way. I think that the need for teachers, probably, of anything greater in order to work with the more nuanced issues and challenges that we face. I think there are a few mitigating factors here that I think do help. I mean, one is that it’s extremely difficult, in my view, to teach a very abstract subject that students don’t connect to the real world very easily. And that’s what we’re asking our math teachers to do at the moment.

If you ask a question, why am I learning how to, our earlier example, how to solve a quadratic equation, it’s actually fairly hard to answer that. And indeed, those teachers often in their real lives are not really using those things. Whereas teachers of English language are using English language in their real lives, they’re reading and writing and doing other things. So I think that the computational thinking I’m talking about, or maths I’m talking about, is starts from the real

world. And I think that actually makes it relatively easier to teach because the attachment point to the real world is easier. And we may be able to also get teachers from other disciplines, let’s say science, maybe things like geography, where they can use computational thinking, and they may be able to help in teaching, in a sense, problem sets, ways to think about it from that angle when they wouldn’t be able to teach a traditional math subject. So it’s more challenging,

it’s different. And one of the differences, I think, as a CEO of a company, it’s like you might have said in years gone by, the sort of traditional boss of a company, as we would have called it in the UK, where you basically ordered people around, said you do this now and you do that now. There’s been a change in any sophisticated high tech company, where it’s a much more nuanced business being a CEO, it’s much more complicated. And yes, it’s more challenging, but I think the world’s

moved forward. That’s a great answer, I appreciate that. It does require a curiosity and humility, the ability to introduce problems that don’t have easy answers. And with students to say, I don’t know the answer to that, how might we approach that problem? So it does feel like a new, not just a new method, but a new mindset to thinking about mathematics and computational thinking across the curriculum. And I think it needs a new, I mean, one thing we need

to really help our educators with is confidence. I mean, I think this word confidence echoes around. And one of the problems with mathematics is sometimes our math teachers aren’t confident, because it’s quite tough. And it’s also, as you say, it’s easy to be sort of proved wrong in a way that perhaps isn’t in English language or in history or something, quite the same way. But I think we can really help that. I also think actually that if there are good aspects, if there

can be good aspects of this horrific pandemic we’re in at the moment, people’s comfort with remote instruction, remote learning, or at least the start of that, I think is quite helpful. Because I think even if you’re in a classroom setting, I think the idea of beaming in others to help shouldn’t be, in a sense, threatening to teachers. We should find a way to do that. It’s like, as I say, running a company, for example, it’s not embarrassing for me to bring

somebody into help when I don’t know the answer to something. And so I think we can restructure how we do that to add the confidence and to be able to help with that process. Here’s a strange tidbit on page 227. You say it’s dumb to use the single-purpose calculators. I appreciated that. Please don’t make your kids buy those $100, $200 single-purpose calculators. Yeah. I mean, again, this is a case where the real world has moved forward. Calculators were

what people used 30 years ago. They were great machines. It’s just that’s not what they use now. No, it’s silly. Don’t do it. All right. Let’s try to close with some advice. So this is going to be super scary for the math teachers that are listening or for the principals that know that’s dumb to torture kids with endless calculation. But they’re scared because they’re in an idiotic test-based accountability system where the only thing that matters is grade

level proficiency as measured by a standardized test score. So for those people, how can they start around the edges to make their math education less idiotic? And how can they supplement it in ways that begin to bring kids into computational thinking? So I think one way is to get this four-step process sort of ingrained in a useful, just make sure that even with problems that seem quite simple, it’s really good to think through this four-step process because I think there’s

a couple of things. I think it adds confidence even to the current what they’ve got to pass. But it also gives you the bigger picture. You’re defining or abstracting or computing, you’re interpreting. You can use that across your curriculum. You can use that in English and social studies and science. Well, yes, because I would say those are subjects where you can have computational English, computational social science, and that is what’s happening. And in fact, in

universities, there are specifically courses and things like that. So I would marry it up even that way. And by the way, we’ve got a poster that you can download. It’s a great poster. A nice sort of computational thinking post. So I would hang that on your wall. And I think point to different bits. I mean, point to the poster was when you’re in a particular part of solving a problem, point to that step. And so that’s a sort of a process to fall back on. And I think this idea of

when you get stuck, you fall back. So I think that confidence will just help hopefully even if you’re doing your traditional math. I think the other way is as much as possible, try to start at least, to some extent, from a real world problem that might try and use the machinery of maths that you’re teaching at that moment. You can’t do that totally because of the maths that we’re locked into right now because of the small number of tool sets, etc. But I think there are places where equations are

useful. There are places where looking at a curve is useful and coming up with an idea of what that is. And I think putting it in context early will really help everybody sort of focus on. Another thing, you know, I’d love, you know, students of course, and particularly students who want to go further. I mean, we’ve put up online some computer based maths modules that start from, so that that’s our computational thinking modules where we’ve tried to start from a question we think we

hope students will find somewhat interesting and work through those. And those are available, both as sort of individual online learning with videos and things, all they can be used by teachers as well. And so we just put a few of those up, which we’ve been trying to build. And I suppose those are a way that if there is a spare time, that I think is a powerful way to expose students to really what the point of all of this is in the end. So those are a few sort of opening.

No, I appreciate that. Let’s shift to the advocacy question. Because with the book launch, you’ve also launched a campaign called the Maths Fixed Campaign for Core Computational Curriculum Change. So that’s another great way to get involved is from an advocacy standpoint. I didn’t encourage all the educators listening to check out this MFC 5 change. Yes, we it’s a and you if you go to the MathsFix.org, you can you can find it from from that front page

as well. My purpose here was to say, you know, in the end, one of the problems is the people who want Math to stay as it is, kind of have one voice to some extent. And for those folks that want to see a reform, have, you know, have so many disparate voices, it’s very tricky for policymakers to know what to do with that. And the point here was to have we’ve got five points that that in this campaign where we’re hopefully things that people will find it easy to agree with.

Obviously, it’s hard to agree. I would like it if people agreed with every single word in my book, but that’s that’s a tall order. And I want people to be skeptical. So but I do think that these five points for many, many people will seem like things they can agree with. And I think if we can go to policymakers and say, look, we’ve got a large body of people who think that this is a change we need to make these five, these five aspects we can we can agree about, I think that

will be very helpful in shifting the risk. Because at the moment, policymakers, we shouldn’t, we may always complain about our policymakers in different places, certainly UK, we often do, but actually, it’s a tough job knowing where to go next. And you have a risk profile for yourself as to, you know, if I do this, I’m going to get into trouble from the folks voting for me or from teachers or from parents or from students or from universities. And so I think that the more we can show there’s

a body who wants have a consistent voice, at least for, you know, core issues, core values, I think that will be really, really helpful in trying to make these important changes. That’s great advice. Start where you can start with a local school community conversation. If you can join a state conversation about revising math standards and math assessments, that’s great. If you can join your National Association of Math Teachers and begin the

dialogue there, that’s great. I think what you hear Conrad and I both saying is, do what you can around the edges in your school today to introduce more computational thinking and then look for ways that you can plug in and make your voice heard at the policy level. Let us know how you get on as well. Yes, please do. Check out the Maths Fix and Education Blueprint for the AI age. Conrad, thanks so much for this book. It’s really,

really timely. It’s really important. We’re going to keep encouraging people to read this book and to get involved. Thanks for being on the podcast. Thanks very much. It’s been a great talking to you and I think it’s great to have an in-depth conversation on these topics. A big thanks to Conrad for joining us on this week’s episode. We highly recommend giving a copy of his book, The Maths Fix, to your local school superintendent and start a community

conversation about the math we require of our kids. For more, listen to episode 239 or Stanford’s Joe Bowler talks about the importance of data science. We’ve got it linked in the show notes and on the blog. All right, listeners, that’s it for this week’s episode. But before you go, make sure you rate and review the podcast and hit subscribe so you’re sure to get all of our future episodes as soon as they drop. Thanks for tuning in for the Getting Smart podcast. This is Jessica,

signing off.