Friday, October 5, 2018

It's high time to re-focus systemic mathematics education - and change the way we assess it

“In math you have to remember, in other subjects you can think about it.” That statement by a female high-school student, was quoted by my Stanford colleague Prof Jo Boaler in her 2009 book What's Math Got To Do With It? I took it as the title of my June 2010 Devlin’s Angle post, which was in part a review of Boaler’s book. In a discussion peppered with quotations similar to that one, Boaler describes the conception of mathematics expressed by the students in the schools where she conducted her research. To those students, math was a seemingly endless succession of (mostly meaningless) rules to be learned and practiced. Among the remarks the students made, are (the highlighting is mine):

“We're usually set a task first and we're taught the skills needed to do the task, and then we get on with the task and we ask the teacher for help.”

“You're just set the task and then you go about it ... you explore the different things, and they help you in doing that ... so different skills are sort of tailored to different tasks.”

“In maths, there's a certain formula to get to, say from A to B, and there's no other way to get it. Or maybe there is, but you've got to remember the formula, you've got to remember it.”

Given that is their experience of mathematics, there is no surprise that many students that are taught that way give up and bail out at the first opportunity. In fact, a more natural question is, “Why do a few students enjoy math and do well in it, answering questions at the board with seeming ease.”

The answer is, the students who do well in math and enjoy it, are doing something very different from the activity described in the above quotes. Indeed, one of the things that attracts students to math is that it is the subject where you have to learn the least number of facts or methods, and can spend most of your time in creative thinking. Those “good” students have discovered that, in the math class, there is relatively little you have to remember; most of the time, you can wing it, and you’ll do just fine. It’s not about learning a wide range of formulas and special techniques, the trick is to learn to think a certain way.

For the few who know the “one big trick” professional mathematicians rely on, math class is an engaging and enjoyable creative experience. How those few get to that point seems to be exposure to an inspiring teacher at some point, hopefully before the rot sets in and the student has been completely turned off math, or perhaps some other fortuitous event. Absent such a stimulus, though, it’s no surprise that when fed a steady diet of math classes focused on mastering one concept, formula, or special technique after another, the majority sooner-or-later give up, and simply endure it (in bored frustration) until they are through with it.

Which brings me to the mathematics Common Core State Standards, rolled out in 2009 to guide developments in education required to meet the changing environment and needs of the 21st Century.

If you go onto the CCSS website, you will find a large database of specific standards items, one such (which I picked at random) being

CCSS.MATH.CONTENT.5.MD.C.5.   Relate volume to the operations of multiplication and addition and solve real world and mathematical problems involving volume.

Parsing out the reference code for this particular standard, it relates to Grade 5, Measurement & Data, Geometric measurement: understand concepts of volume, item 5.

It is important to realize that the Common Core is not a curriculum, nor does it stipulate how any topic should be taught. It is exactly what its name indicates: a set of standards that educators should aim to meet at each stage. But it is tempting to view it as a list of specific topics that a teacher should cover one after another. (Tempting, but not easy if you are working from the website since it isn’t presented as a list. I suspect that is deliberate.) If you do that, then there is an obvious danger that the result will be a continuation of the approach to math education that students experience as a process of learning one little trick after another, and you are back with the situation Boaler catalogued.

But those individual CC items are the terminal-nodes on a branching tree that has a regular structure, and it is in that structure that you see not only order but just a handful of basic principles. It is those basic principles that should guide math instruction. There are just eight of them. They are called the Common Core State Standards for Mathematical Practice. Here they are:
1. Make sense of problems and persevere in solving them.
2. Reason abstractly and quantitatively.
3. Construct viable arguments and critique the reasoning of others.
4. Model with mathematics.
5. Use appropriate tools strategically.
6. Attend to precision.
7. Look for and make use of structure.
8. Look for and express regularity in repeated reasoning.
Those eight principles (the website elaborates on each one) constitute the core of the mathematics Common Core. They encapsulate the key features of mathematics learning essential for anyone living or working in today’s world. Notice that there is nothing about having to learn specific facts, formulas, or techniques. The focus is entirely on thinking.

The same is true of the specific items you will find in the rest of the Common Core website. When you drill down, you will find targets to aim for in order to develop thinking, following those eight principles at each grade level.

When mathematics is taught as a way of thinking, along the lines specified in those eight Common Core principles, then along the way, a student will in fact pick up a whole range of facts, and meet and learn to use a variety of formulas and techniques. But the human brain does that naturally, as an automatic consequence of lived experience. We are hardwired that way!

In contrast, learning becomes hard when presented as a sequence of items to be learned and practiced one by one, each in isolation, based on the false premise that you must first learn the “basics” before you can “put them together” to form the whole. The moment you realize that mathematics is about process rather than content, about doing rather than knowing, the absurdity of the “must master the basics first” philosophy becomes apparent.

Notice I am not saying “the basics” are irrelevant. Rather, they are picked up far more easily, and in a robust fashion that will last a lifetime, by using them as part of living experience. For sure, a good teacher can speed the process up by helping a student recognize the used-all-the-time basics, and maybe provide instruction on how they can be used in other contexts. But the focus at all times should be on the thinking process. Because that’s what mathematics is!

If the above paragraph sounds a bit like learning to ride a bike, then all to the good. A child learning to ride a bike will acquire a good understanding of gravity, friction, mechanical advantage, and a host of other physics basics. An understanding that a physics teacher can use to motivate and exemplify lessons in those notions. But no one would say that you cannot learn to ride a bike until you have mastered those basics! Think of doing math as a mental equivalent to riding a bike. (I wrote about this parallel in Devlin’s Angle before, in my March 2014 post. My final point there was somewhat speculative, but as a mathematician who also rides bikes, I claim that the overall parallel between the two activities is very strong and illuminating.)

Now comes the point where I part company with the CCSS, and indeed much of the focus in present-day American mathematics education and standardized math assessment.

Let me ease myself in by way of my cycling. I learned to ride a bike as a child and used a bike to get around throughout my entire childhood up to graduation from high school. I then hardly ever got on a bike again until I was 55 years old and my knees gave out after a quarter century of serious running, and I bought my first (racing-style) road bike. For the first twenty minutes or so, I felt a bit unsteady on my new recreational toy, but I did not need to seek instruction or help in order to get on it that first time and ride. The basic bike-riding skill I had mastered as a small child was still there, available instinctively, albeit a bit rusty and in need of a bit of adjusting for the first few minutes.

Moreover, when I started riding with a local club, my fellow riders gave me lots of tips and advice that made me able to ride more safely and at higher speeds. Some of what I learned was not obvious, and I had to practice. It was not the same as riding a city bike at low speed.

Likewise, when I bought, first, a mountain bike, and then a gravel bike, I had to take my basic bike-riding ability and transfer it to a different device and different kinds of terrain, and, in each case, once again learn from experts how to make good, safe use of my new machine.

The point is, they were all bicycles and it was all cycling. So too with mathematics. Once someone has mastered — truly mastered — one part of mathematics, it is relatively easy to master another. Yes, you will need to learn some new things, including a new vocabulary, some new techniques, and likely a new ontology, and yes you will almost certainly benefit from (and possibly need) help, guidance, and advice from experts in that new area of math. But you already have the one key, crucial ability: you can think like a mathematician.

In terms of learning mathematics, what this means is that it is enough to devote considerable effort to genuinely mastering just one topic — say elementary arithmetic — and then spending some time going through the process of branching out from that one area to a number of others (perhaps algebra, geometry, trigonometry, and probability theory).

In terms of mathematics assessment, it means that it is enough to test students’ mathematical thinking ability focused on just one topic, and then test to see if they have sufficient knowledge of a number of other topics. The advantage of approaching assessment this way is that, at least with current assessment methods, testing thinking is time-consuming and expensive, since it requires a small army of trained human assessors to grade solutions to open-ended questions, often “complex performance tasks,” whereas assessing breadth of knowledge can be done with a variety of machine-grades tests. So there are significant savings in cost and time if assessing thinking ability is done separately on just one topic. Which is absolutely all that is required, since the ability to think mathematically is just like the ability to ride a bike — once someone can ride one kind of bike, they can, with perhaps some adjustment, ride any kind. There is absolutely no need to test for that. As a result of natural selection of many thousands of years, humans can all do it.

The only question that remains is what mathematical topic should we focus on to develop the ability to think mathematically — including, I should add, an understanding of the importance of the precise use of language, the ability to handle abstraction, the need for formal definitions, and the nature and significance of proof.

Well, why not once again take our cue from how most of us learn to ride a bike. What is the equivalent of our first child’s bicycle? Elementary arithmetic.

What’s that you say? “There isn’t enough meat in elementary arithmetic to learn all you need to know about thinking mathematically, with all those bells and whistles I just mentioned.” Think again. Alternatively, check out the article written by mathematics educator Liping Ma in the article she published in the November 2013 issue of the Notices of the American Mathematical Society, titled A Critique of the Structure of U.S. Elementary School Mathematics.

Based on her experience with mathematics education in China, Ma argues forcefully, and effectively, that there is more than enough depth and breadth in “school arithmetic” (as she calls it) to fully develop the ability to think mathematically. True, in the West we don’t teach elementary arithmetic that way; indeed, we present it as a series of basic number facts to be memorized and algorithms to be practiced, as in the Boaler critique. We do so, at some speed I should add, in large part because we are in so much of a hurry to move on to all the other mathematical topics that someone at some time in the past declared were “essential” to learn in school. But as many have pointed out over several decades, the result is that our mathematics curriculum is “a mile wide and an inch deep”, resulting in students leaving school believing that “In math you have to remember, in other subjects you can think about it.”

In the June 2010 Devlin’s Angle post I referred to earlier, where I talked about Boaler’s then-new book, I mentioned Ma, and said I agreed with her argument about using school arithmetic as the topic to develop the ability to think mathematically. I still do.

I also think school arithmetic provides the one topic you need to assess mathematical thinking ability — regardless of whether you are assessing student learning, teacher performance, or district system performance. Given that, assessment of whatever breadth is required can be done relatively easily and cheaply. Because the thinking part is essentially the same, the assessment of the breadth can focus on what is known (rather than what can be done with that knowledge).

And (of course), one really valuable benefit of focusing on school arithmetic is that it provides as level a playing field as you can hope for, with elementary arithmetic the one mathematical topic that everyone is exposed to at an early age.

In a future post, I’ll take this topic further, looking at the implications for teaching, the educational support infrastructure (including textbooks), the effective use of modern technologies, and the educational implications of those technologies.

Also, as the title makes clear, the focus of this article has been systemic mathematics education, the mathematics that states decide is essential for all future citizens to learn in order to survive and prosper and contribute to society. There is a whole other area of mathematics education, where the focus is on the subject as an important part of human culture. That’s actually the area where I have devoted most of my efforts over the years, writing books and articles, giving public talks, and participating in radio and television programs. So I’ll leave that for other times and other places.

Tuesday, September 4, 2018

Is math really beautiful?

The above tweet caught my eye recently. The author is a National Board Certified mathematics teacher in New York City who has an active social media presence. Is his claim correct? Not surprisingly, a number of other mathematics educators responded, and in the course of the exchange, the author modified his claim to include the word “just”, as in “It isn’t just about beauty …” In which case, I think he is absolutely correct.

Like many mathematicians who engage in public outreach, I have frequently discussed the inherent elegance and beauty of mathematics, the wonder of its purity, and the power of its abstraction. And as a body of human knowledge, I maintain (as do pretty well all other mathematicians) that such descriptions of the subject known as pure mathematics are totally justified. (Cue: for the standard quotation, Google “Bertrand Russell mathematical beauty”.) Anyone who is unable to recognize it as such surely has not (yet) understood what (pure) mathematics is truly about.

In contrast, the activity of doing mathematics is indeed “messy,” as Pershan claims. That is the case not only for the activity of using mathematics to solve problems in the real world, but also the activity of engaging in pure mathematics research. The former activity is messy because the world is. The latter is messy because the logical elegance and beauty of (many) mathematical theories and proofs are characteristics of the finished product, not the process of development.

And there, surely, we have the motivation for Pershan’s comment. When we teach mathematics to beginners, we don’t do them any service by making claims about beauty and elegance if what they are experiencing is anything but. With good teaching of a well-designed curriculum, we can ensure that they are exposed to the beauty, of course, and perhaps experience the elegance. But it’s surely better to let them know that the messiness, the uncertainty, the repeated stumbles, and the blind allies they are encountering are part of the package of doing mathematics that the pros experience all the time, whether the doing is trying to prove a theorem or using mathematics to solve a real-world problem.

By chance, the same day I read that tweet, I came across an excellent online article on Medium about the huge demand for mathematical thinking in today’s data-rich and data-driven world. Like me, the author is a pure mathematician who, later in his career, became involved in using mathematics and mathematical thinking in working on complex real-world problems. I strongly recommend it. Not only does it convey the inherent messiness of real-world problems, it convincingly makes the case that without at least one good mathematical thinker on the team, management decisions based on numerical data can go badly astray. As the author states in a final footnote, he takes pleasure in the process of applying the rigor of mathematics to the complex messiness of real-world problems.

To my mind, therein lies another kind of mathematical beauty: the beauty of making productive use of the interplay between the abstract purity of formal rigor and the messy stuff of everyday life.

Wednesday, August 8, 2018

How a Fields Medal led to a mathematical roller-coaster journey

By Keith Devlin

First, congratulations to Caucher Birkar, Alessio Figalli, Peter Scholze, and Akshay Venkatesh on being awarded the Fields Medal, an award that for regular “Devlin’s Angle” readers needs neither introduction nor description. (If it does, use Google.)

With Fields Medals awarded only (at most) once every four years to mathematicians who produce truly exceptional mathematics before they turn forty, few of us who enter the field come close to getting one. (Indeed, in some ways they are more akin to Olympic Gold Medals than the Nobel Prizes with which they are usually compared. Few club athletes will get one of those either.)  On the other hand, many of us earn our doctorates, or build our careers, by understanding a new approach or mastering a new technique that led to a Fields Medal. Just as Bill Gates copied the groundbreaking Macintosh interface to create Windows, so too it can pay off handsomely, and often quickly, for a young mathematician to “reverse-engineer” a Fields-Medal-winning new result and try to use it to solve a different – though often related – problem.

In fact, sometimes, the medal-winning breakthrough has such broad applicability that is initiates an entire new subfield of mathematics. That was exactly how I began my mathematical career almost a half a century ago. An interest in computing, initiated by a high-school summer internship writing software for British Petroleum (using the very first digital computer delivered to the city I grew up in), stayed with me throughout my undergraduate years, culminating with me interviewing for a job at IBM on graduation. But I was put off by the strong corporate culture and the lack of intellectual freedom I feared would come with joining Big Blue. Instead, I decided to go for a PhD in the general area of computing. Unfortunately, this was before Computer Science was a recognized discipline, and though it was possible to pursue graduate research related to computing, mathematically speaking there was not much of a “there” there back then.

The one mathematically-intriguing little “there” was a relatively new subject called Automata Theory that I had come across references to. Moreover, one of the pioneers of that field, John Shepherdson, was a professor of mathematics at the University of Bristol, just over a hundred miles from London, where I had just graduated. As chair of department, Shepherdson had built up a strong research team of experts in different branches of Mathematical Logic, the subfield of mathematics that provided the mathematical tools for Automata Theory. And so it was then that I applied to do a PhD at Bristol. That was in the fall of 1968.

Once I arrived in Bristol, everything changed. Among the mathematics graduate-student community at Bristol University, all the buzz – and there was a lot of it – was about an emerging new field called Axiomatic Set Theory. Actually, the field itself was not new. But as a result of a Fields Medal winning new result, it had recently blossomed into an exciting new area of research.

Not long after Georg Cantor’s introduction of abstract Set Theory in the late 19th century, Bertrand Russell came up with his famous paradox, concerning the set of all sets that are not members of themselves. To escape from the paradox – more accurately, to rescue the appealing, natural notion of using abstract sets as the basic building block out of which to construct all mathematical objects – Ernst Zermelo formulated a seemingly simple set of axioms to legislate the formation of sets. With an important addition from Abraham Fraenkel in 1925, that axiom system seemed to provide an adequate basis for the construction of all the objects of mathematics, while avoiding Russell’s Paradox. Zermelo-Fraenkel Set Theory, as it became known, rapidly came to be regarded as the "Grand Unified Theory" of mathematics, the basic system on which everything else is built. It was generally referred to as ZFC, the “C” denoting the Axiom of Choice, a basic principle Zermelo included but which was sufficiently controversial that its use was often acknowledged explicitly.

While the ZFC axiom system was indeed sufficient to ground all of mathematics, there were a small number of seemingly-simple questions about sets that no one could answer using just those axioms. The most notorious by far went back to Cantor himself: Cantor’s Continuum Problem asks how many real numbers there are? Of course, one answer is that there are an infinite number of such. But the ZFC axioms allow the construction of an entire system of infinite numbers of increasing size, together with an arithmetic, that can be used to provide a “count” of any set whatsoever. The smallest such infinite number, aleph-0, is the number of natural numbers. After aleph-0, the next infinite number is aleph-1. Then aleph-2, and so on. (It’s actually a lot more complicated than that, but let’s leave that to one side for now.)

Cantor showed that the real continuum, the set of all real numbers, has an infinite size strictly larger than aleph-0, so it must be at least aleph-1. But which aleph exactly was it? It’s tempting (on the grounds of pure laziness) to assume it’s aleph-1, an assumption known as the Continuum Hypothesis (CH). But there is no known evidence to support such an assumption.

In 1940, Kurt Goedel contructed a set-theoretic model of ZFC in which CH is true, thereby demonstrating that CH could never be proved false. But that does not imply that it is true. Maybe it was possible to construct another model in which CH was false. If so, then CH would be completely undecidable, based on the ZFC axioms. This would mean that the ZFC axioms are not sufficient to answer all reasonable questions about sets.

In 1963, Paul Cohen, a young mathematician at Stanford University, found such a model. Using an ingenious new method for constructing models of set theory that he called forcing, Cohen was able to create a model of ZFC in which CH is false. That result earned him the Fields Medal in 1966.

By 1968, when I went to the University of Bristol to commence my doctoral work, Cohen’s new method of forcing had been shown to have wide applicability, making it possible to prove that a number of long-standing, unanswered mathematical questions were in fact undecidable in the ZFC system. This opened up an exciting new pathway to getting a PhD. Learn how to use the method of forcing and then start applying it to unsolved mathematical problems, of which there was no shortage. Large numbers of beginning graduate students did just that, and by the time I joined a group of them, a few months after arriving at Bristol, the field was red hot. My interest in computation did not go away, but it would be over two decades before I would pick it up again. At 21 years of age, with a newly minted bachelors degree in mathematics under my belt, I had a mathematical research career to build, and axiomatic set theory was by far the most exciting field to do it in. I jumped onto the roller coaster and joined in the fun.

Working in my newly chosen field was just like working in any other branch of mathematics. Each day, you woke up and attempted to prove various mathematical statements using logically rigorous reasoning. To an observer looking over your shoulder, doing that involved scribbling formulas on paper and manipulating them in an attempt to construct a proof, just like any other branch of mathematics. The “rules of the game” were exactly the same as in any other branch of mathematics as well. The only difference was the nature of the answers you obtained – on the rare occasion when you did so. (Mathematics research is 95% failure. Actually, the failure rate may be higher than that; we have a far worse batting average than any professional baseball hitter.) In what those of us in this new field called “classical mathematics,” the goal was to prove statements about mathematical objects were true or false. In the new mathematics of undecidability proofs, the goal was to prove that statements about mathematical objects were undecidable (in the ZFC system). In both cases, the result was a rigorous mathematical statement (a theorem) justified by a rigorous mathematical argument (a proof).

From the perspective of mathematics as a whole, this meant that, thanks to Cohen, mathematicians had a new way to answer a mathematical question. Classically, there had been just two possibilities: true and false. If you can do neither, you had failed to find an answer.  Now, there was a third possibility: (provably) undecidable. What had previously been failure could now become success. Absence of a definite answer could be replaced by getting a definitive answer. Lack of knowledge could be replaced by knowledge.

The two decades following Cohen saw a whole range of unsolved mathematical problems proved undecidable, as a whole army of us jumped into the fray. Some results were easy. Success came quickly to those smart enough or lucky enough (or both) to find an unsolved problem that yielded relatively easily to the forcing technique. Others took much longer to resolve, and a few resisted all attempts (and have done so to this day). But by the start of the 1980s, the probability of success had dropped to that in other areas of mathematics. From then on, for most young mathematicians, getting a PhD by solving an undecidability problem meant finding some relatively minor variant of a result someone else had already obtained. The party was over.

Looking back, I realize that I was simply very lucky to be starting my mathematical career when a productive new subfield was just starting up. By going to the University of Bristol to do a PhD in Automata Theory, I found myself in the right place at the right time to jump ship and have the time of my life. When the field started to settle down and slowdown in the 1980s, I started to lose interest. Not in mathematics, just in that particular area as my main research focus. My main interest shifted elsewhere as my attention was caught by some new mathematics being developed at Stanford (as with forcing, Stanford turned out to be the place that generated the new ideas that caught my attention). That new mathematics was closely intertwined with my earlier high school interest in computing. But that’s another story.

Thursday, July 5, 2018

By Keith Devlin

21st Century Math: The Movie

All my Devlin’s Angle posts this year so far have studied the dramatic shift that took place over the past twenty-five years in the way professional mathematicians “do the math” in order to solve real-world problems. There have been parallel changes in the way pure mathematicians work as well, but those changes are somewhat less visible, and not as dramatic. In any case, I have been focusing on mathematics in the wild.

Those changes in how math is done have put pressure on global education systems to catch up. In previous posts, I addressed these changing educational needs, but overall, there has been a considerable lag. In the United States, many of the better, selective, private schools have adjusted, but little has changed in the math classrooms of most state-funded schools. There are a number of reasons for that lack of action, some educationally valid, others resulting from Americans’ proclivity to treat mathematics education as a political football. But that is another story.

The fact is, however, the mathematical world has changed significantly, it is not going to change back, and sooner or later the educational system must catch up. Hopefully sooner, given that today’s students will enter a world and a workforce where no one does calculations anymore – where by “calculation” I mean performing any form of algorithmic procedure.

In May, I participated in Maths for the 21st Century, a global mathematics education summit in Geneva, Switzerland, organized to discuss the new way mathematics is being done and how best to prepare students to live and work in such a world. Both the United States Department of Education and the OECD’s (Organization for Economic Cooperation and Development’s) PISA educational testing organization were represented at the summit.

The half hour talk I gave at the summit is in many ways a summary (absent all the details) of my series of posts for the MAA. So I thought I would wrap up the series, at least for now, by pointing you to the video of my presentation. The main summit page, linked above, also provides a link to a lightly abridged PDF version of the deck I used to accompany my talk.

My experience in giving public talks on this topic over the past several years has been that it evokes two very different reactions. Engineers and scientists in the audience, for the most part, nod along in agreement with everything I say. I am, after all, just describing the way they have been working for twenty years. In contrast, teachers, or at least a great many of them, often show surprise, confusion, and not infrequently hostility. Many parents react similarly.

Why is that? Well, to repeat an arguably over-used quotation from the great Paul Newman movie Cool Hand Luke, “What we have here is failure to communicate.” After my talks, I am often left feeling like the Paul Newman character, Luke, in that clip. However, for the analogy to work, Luke has to represent not me but the entire mathematics community.

Teachers are taken aback to be told that calculation is less relevant in today’s world. I believe this is because no one in the mathematics business – that is, the business of using mathematics to solve real-world problems – has taken the trouble to inform teachers that the entire game has changed, and in what ways. It’s time we bring better communication to this issue. My series of Devlin’s Angle posts this year is one of my latest attempts to do just that. The Geneva video I am directing you to is another.

Wednesday, June 6, 2018

Cycling can be such a drag – and math can tell you exactly how much

By Keith Devlin

Last month, my two greatest passions collided: mathematics and cycling. On my way back from a short biking trip to the Californian Central Coast (gorgeous in the late spring, when the grass is still green and the wildflowers are in full bloom), I stopped off in Morgan Hill (home of the American Institute of Mathematics) to watch part of Stage 4 of the seven stage, Amgen Tour of California. Wandering around the start area, where the teams warm up on stationary trainers and the big vendors show off their wares, I noticed a couple of guys in one corner, promoting a curious looking bike.

Actually, it wasn’t the bike itself that looked unusual, it was the strange, overly large fenders over the two wheels. Had the bike been in a regular bike shop, I would have thought they were splash guards for image-conscious, athletic-leaning commuters to use on rainy days. But the exhibits at a professional cycle racing event are aimed at hard-core cyclists, whose passion revolves around razor-thin saddles, aerodynamic bike design, and low bicycle weight. Whatever those fenders were for, they were not for keeping a rider dry. It had to be about performance. But modern, racing-bikes are designed to be as light and thin as possible – the wheels have tires just a tad over 20mm wide – so those bulky-looking fenders seemed completely out of place. I could not resist asking for an explanation.

The conversation soon got very mathy, and I quickly sensed an opportunity for a Devlin’s Angle post showing the power of thinking mathematically about everyday activities—be those activities work- or leisure- related. It would be a natural continuation of my recent series of posts (starting in January) on how mathematics education needs to change to prepare people for life in the 21st century.

The strange looking fenders were designed by Garth Magee, a former aerospace engineer from Southern California, who like me is a keen cyclist. I had already gotten some clue as to what the fenders were for from the company name on a poster: Null Winds Technology. The fenders must have something to do with reducing drag. (If so, then we need to start calling them “fairings”.) That would also explain Magee’s presence at the Time Trial stage of the Tour of California. Modern professional time-trialing is all about aerodynamic bicycle design, with all the major international bicycle manufacturers spending small fortunes on computer-aided designs and hours of testing in wind tunnels. Magee’s main purpose in Morgan Hill was likely to raise interest from some of the other manufacturers present at the event, I surmised. In addition, professional time-trialing is heavily regulated to ensure competitive cycling’s “purity” and “fairness”, and any kind of wheel fairings are banned in competition, so Magee’s product was surely not aimed at professional racers. He was likely seeking to sell to amateur riders, like myself.

[Disclaimer: Magee told me he had formed his company in 2012 to turn his discovery/invention into a product people can use. I have no involvement with the company, I don’t own any of their products, and I am not actively promoting their products. My focus here is on the really cool application of mathematical thinking.]

From past research into what was known about the mathematics of cycling, I knew it is complicated, uses advanced techniques, and has yet to fully explain the physics of cycling. You can get a sense of just how advanced and complicated it is by perusing the description on Wikipedia.

As any experienced cyclist will tell you, at anything beyond very low speeds, the greatest resistance to forward motion is due to the air. That’s why professional cycle racers stay close together in tight packs (“pelotons”) or long lines (“pacelines”), where the few rides at the front sacrifice their chance of winning in order to shield their teammates from the wind. In still air, the headwind is caused entirely by the cyclist’s own forward motion (at speeds up to 30 mph on the flat). If there is a headwind, the resistance is greater. Moreover, it increases with the cube of the rider’s speed relative to the air. That’s why riding a bike into a strong head wind is so hard. The Wikipedia article gives you the key formula, which I reproduce below.

Another nice summary of the relevant math you can access online can be found here.

What the math tells you is that wind drag is a huge problem that every cyclist encounters when trying to go faster. At a speed of approximately 7 mph, overcoming air resistance takes about half of your effort (with ground friction on the tires and mechanical resistance in the drive train accounting for the other half). As you go faster, that cubic growth starts to flex its muscles, which means you have to increasingly flex your muscles in order to overcome air resistance that demands a larger and larger proportion of your total effort. At around 15 mph, approximately 70% of your effort is being used to overcome air resistance; at 20 mph it takes roughly 85% of your effort. At the top end, a typical average speed for a flat stage in the Tour de France is about 29 mph. At that speed, over 90% of the effort needed to maintain this speed is used to overcome air resistance.

Needless to say, the bicycle manufacturing industry has put in a lot of effort over the years to try to minimize the effect of headwind drag. The results of those efforts, explained for cyclists rather than mathematicians, are nicely summarized in two articles that you will find online at: https://tunedintocycling.com/2014/06/28/aerodynamics-part-1-air-resistance/ and https://tunedintocycling.com/2014/07/25/aerodynamics-part-2-small-things-that-reduce-air-resistance-and-drag/.

With so much research put into the problem of headwind drag, you would think the industry had done as much as could be done. But as Magee showed, there were still more efficiencies to be obtained. His observation is an excellent illustration of the power of mathematical thinking.

The mathematics I’ve summarized so far treats the bicycle as a single item, not an assembly of components. (An instance of the mathematician’s standard approach, as encapsulated in the quip “Consider a spherical cow not subject to frictional forces.”) Magee focused on the effect of headwind on the wheels.

To be sure, he was not the first to do that. Most of the racers at the Morgan Hill time-trial rode bicycles with solid rear wheels. Though considerably heavier than the more common spoked wheels, a solid wheel creates far less rotational drag than does a regular wheel, where the spokes create turbulence as they cut through the air. (The only reason the pros don’t use a solid wheel at the front as well is because it would make the bicycle highly unstable, with a slight crosswind likely to send the bike and the cyclist out of control. In indoor races on banked tracks, you do see bikes with two solid wheels.)

And now, as that last paragraph should indicate (note the word turbulence), we are deep into aerodynamics. The field Magee worked in for many years. What Magee did, that no one had previously done (at least to the point of taking out a patent on a design), was observe that, with wind resistance increasing with the cube of the speed through the air, the resistance increases rapidly as you go up the wheel from the axle to the top of the wheel (where the top of the tire is moving at twice the speed of the bicycle), and as you go down from the axle to the ground it decreases to 0 relative to the ground. What would happen, Magee asked, if a shield were to keep the wind from hitting the fast-moving top portion of the wheel? Sure, it would add some weight, but with that cubic function to contend with, it seemed likely the drag reduction could be significant.

In particular, what portion of the wheel should be protected to optimize any gain due to overall reduced wind resistance on the wheel? This is where a bit of good-old-fashioned math comes into play. The chart below is the key to Magee’s fairing design.

According to the math, there would be little or no benefit when riding in still air (or with a tailwind). But in a headwind, the difference should be noticeable to the rider. The stronger the headwind, the greater the benefit. To test his invention out, he turned to his friend Robert Keating, a former bike racer and triathlete who teaches triathlon and works at a local Triathlon shop in Los Angeles. (I met Keating at the Morgan Hill event, where he and Magee were jointly demoing the new device.) A number of road tests showed that the idea worked as the math said it should. You can see a video of one recent test here. This, folks, is the power of mathematics in action.

Just as with the UPS routing problem I had the students at Nueva School look at in January (see my previous posts for January 2018 onwards), Magee’s problem was all about optimization. Not unique right answers; rather better performance.

Yes, there was a fair amount of sophisticated calculation to be done. But the key was to approach the problem in the right way, so that mathematical power could do its work.

Note, too, that all the background mathematics needed to solve the design problem can be found on the internet. Indeed, in writing this article, I simply used Google to locate suitable sources. (Remember, Google was the very first modern tool on that chart of modern tools to do mathematics I presented in my February post.)

As I keep saying, in today’s world, in using mathematical thinking to help solve a problem, you (usually) don’t need to re-invent the wheel. Those days are largely gone. Today, you mostly need to understand mathematics in a fundamental, conceptual way so you can make an existing wheel do the work for you.

In Magee’s case, of course, that was true both literally as well as in my original metaphorical sense.

All in all, it’s a superb example of 21st century mathematical problem solving.

Finally, I note that I was originally motivated to reverse-engineer the UPS routing algorithm because of the strange movements (and non-movements) of a bicycle I was shipping from California to Princeton. So might it be that math is particularly well suited to problems involving bicycles. Not at all. Bicycles figure in both examples because they interest me. After all, I was only at the Tour of California Time Trial because I am passionate about cycling, and it was my mathematical bent that prompted me to approach the Null Winds display and ask for an explanation. (Remember also my 2014 Devlin’s Angle post on mountain biking and proving theorems.)

The real lesson is that mathematical thinking can be applied to almost anything, particularly if the question is “Can we make it better (in some way)?” You are not interested in biking? Fine. Think about something that really does interest you. The chances are high—very high—that mathematics could be used to make it better in some way.

Once you have decided what to optimize, use the wide range of tools that are now freely available to start to find out how you might do it. Go as far as you can, then seek help from a mathematician. Your passion, experience, and domain knowledge coupled with the mathematician’s experience at using math to solve problems can make a powerful team.

Of course, to proceed this way, as I discussed in my earlier posts this year, you do need to understand what mathematics (really) is and how it can be used. But that is really all you need. Yes, it’s a big “all”. But it’s THE “all”. That’s why it needs to be the focus of mathematics education in the 21st century.

LABELS: mathematical thinking, problem solving, aerodynamics, wind resistance, bicycle mathematics.

Wednesday, May 2, 2018

Calculation was the price we used to have to pay to do mathematics

By Keith Devlin

Ever since mathematics got properly underway around 3,000 years ago, there was only one way to achieve access to the field. You had to spend many years developing a fairly extensive calculation skillset. In the first instance, to pass the graduation and entrance examinations to gain initial access to the field. Then, once accepted into the world of mathematics, calculation of one kind or another was what all mathematicians spent the bulk of their mathematical time doing. Arguably, for most of mathematics history, the subject really was, to a large extent, primarily about calculation of one form or another. Newton, Leibniz, Bernoulli (any of them), Fermat, Euler, Riemann, Gauss, and the other greats of times past, were all superb masters of calculation. (We should also include Boole, since his famous Boolean algebra is also a calculation system.)

But whereas most laypersons seem to think that calculation is all there is to mathematics, surely none of the greats did. Calculation was an important tool (more accurately, a set of tools) you needed to do mathematics, they must have realized, but the essence of mathematics is much more, a plateau of knowledge that transcends all the calculation techniques.

In the 19th Century, that somewhat tacit understanding became explicit. The increasing complexity of the problems mathematicians tackled led to a series of results that defied the human intuition. (Several of them were referred to as “paradoxes”.) This led to an intense period of mathematical introspection, where the primary focus was not performing a calculation or computing an answer, but formulating and understanding abstract concepts and relationships. In other words, a shift in emphasis from doing to understanding. What had previously been implicit, became full-on explicit.

Mathematical objects were no longer thought of as given primarily by formulas, but rather as carriers of conceptual properties. Proving something was no longer a matter of transforming terms in accordance with rules, but a process of logical deduction from concepts. Mathematics was reconceptualized as “thinking in concepts” (Denken in Begriffen).

This was, in every sense, a mathematical revolution, with the primary revolutionaries being
leading mathematicians such as Lejeune Dirichlet, Richard Dedekind, Augustin-Louis Cauchy, Karl Weierstrass, and Bernhard Riemann.

To give just one instance of the shift, prior to the nineteenth century, mathematicians were used to the fact that a formula such as  y = x2 + 3x – 5  specifies a function that produces a new number y from any given number x. Then the revolutionary Dirichlet came along and said, forget the formula and concentrate on what the function does in terms of input-output behavior. A function, according to Dirichlet, is any rule that produces new numbers from old. The rule does not have to be specified by an algebraic formula. In fact, there's no reason to restrict your attention to numbers. A function can be any rule that takes objects of one kind and produces new objects from them.

This definition legitimized functions such as the one defined on real numbers by the rule:
If x is rational, set f(x) = 0; if x is irrational, set f(x) = 1.

Of course, you cannot draw a graph of such a monster. Instead, mathematicians began to study the properties of abstract functions, specified not by some formula but by their behavior. For example, you can investigate questions such as, is the function one-one, injective, surjective, continuous, differentiable, etc.?

For any mathematician alive today, mathematics is a subject that studies formally-defined concepts, with a focus on the establishment of truth (based on accepted axioms), with various forms of calculation (numerical, algebraic, set-theoretic, logical, etc.) being tools developed and used in the pursuit of those goals. That’s the only kind of mathematics we have known.

Except, that is, when we were at school. By and large, the 19th Century revolution in mathematics did not permeate the world’s school systems, which remained firmly in the “mathematics is about calculation” mindset. The one attempt to bring the school system into the modern age (in the US, the UK, and a few other countries), was the 1960s “New Math”. Though well-intentioned, its rollout was disastrous, in large part because very few teachers understood what it was about – and hence could not teach it well. The confusion caused to parents (other than mathematician parents) was nicely encapsulated by the satirical songwriter and singer Tom Lehrer (who taught mathematics at Harvard, and did understand New Math), in his hilarious, and pointedly accurate, song New Math.

As a result of the initial chaos, the initiative was quickly dropped, and school math remained largely unchanged while real-world uses of mathematics kept steadily changing, leaving the schools increasingly separated from the way people did math in their jobs. Eventually, the separation blew up into a full-fledged divorce. That occurred in the late 1980s. The divorce was finalized on June 23, 1988. That was the date when Steve Wolfram released his mammoth software package Mathematica. Within a few short years of that release, if not on the release-date, Mathematica (and a similar package released a few months later in Canada, Maple) could answer pretty well any school or university math exam question with at least a grade B+, and very often an A.

The days when calculation (of pretty well any kind, not just numerical) was the price humans had to pay to do mathematics were over.

Given that thirty years have passed since that initial epochal moment, and most of the world has still not woken up to the fact that the entire mathematical world has changed dramatically and forever, let me repeat the core of that statement in caps.

THE DAYS WHEN CALCULATION WAS THE PRICE HUMANS HAD TO PAY TO DO MATHEMATICS ARE OVER.

To be sure, after that symbolic 1988 date, it took a few years for the change to percolate through the system, gain momentum, and eventually reach critical mass. Three further developments were also hugely significant: the birth of the World Wide Web in 1989 and the browser in 1993, and the launch of Wolfram Alpha in 2009. (Others might want to add other factors. I’m being selective here.)

Talking about being selective, I’ve mentioned Wolfram products twice now. Though I was a member of Wolfram’s Mathematica Advisory Board in the first few years, I have no stake in or involvement with the company. While both Mathematica and Alpha were indeed major players in changing the way mathematics is done – particularly in applied settings – I am citing those particular products largely as icons, using two specific products to represent a range of new digital tools that were being developed around the world at that time. While Wolfram’s systems were ones I myself made early use of in my work, other mathematicians were also active in that digital mathematical revolution, using different systems. Still, Mathematica was the system that caught the public attention.

Since the turn of the new Millennium, I doubt if anyone making professional use of mathematics in their job, or indeed any adult using mathematics in their everyday lives, has taken out paper-and-pencil and followed a classical algorithm to add, subtract, multiply or divide numbers in an array of real-life size, or perform complex algebraic reasoning to solve systems of equations, or solve problems using calculus, or any other established mathematical procedure. Not only would it now be a waste of valuable human time and energy doing something a cheap machine can do in far less time with no possibility of error, but many of the problems that people encounter in their careers and lives have simply too much data for the human mind to handle. Those same digital tools that have made the execution of mathematical procedures unnecessary have also come to dominate and drive our world, so many of the problems that require mathematics in their solution are now simply beyond human capacity. That’s why Amazon Web Services has become such a behemoth for data storage and processing.

But that does not mean humans no longer need to have some mathematical skills. On the contrary, they are as crucial as ever – unless you are willing to be totally reliant on others, but personally, I have never felt comfortable doing that with things that are part of my life every day. What has changed are the specific mathematical skills required today. There are plenty of things computers cannot do or do poorly. Genuinely creative thinking and analogical reasoning are two obvious ones – though with today’s massive cloud computing resources we can use systems that provide an approximation often adequate for the purpose, and on occasion can be better than humans.

Mostly, however, where you need humans is going from a real-world challenge situation to formulating one or mathematical tasks that can help you make progress. Sometimes, progress means solving a real-world problem in the sense of getting a specific answer (say, a number), but much more commonly it’s about finding a better method, where “better” can mean faster, cheaper, safer, or whatever other criterion is important, and where the change may involve developing a new method or improving an existing one.

This way of using mathematics was the focus of that mini-course I gave at a California school (Nueva School) in January of this year, that I wrote about in the February, March, and April posts to this column.

Though several mathematicians and mathematics education scholars expressed agreement with what I wrote, my articles brought some critiques from teachers and parents. The critiques all made reference to my asides about the Common Core State Standards in the first two of the posts. Since “Devlin’s Angle” no longer seems to be a target for the CCSS social media trolls (likely because the yield of issues to react to relative to the length and substance of most of my posts makes it less rewarding to them), I made some efforts to find out what exactly it is about the CCSSs that they objected to. As far as I could ascertain, the issue was inevitably (and predictably) to do with particular implementations of the Standards in specific curricula or (and this seems to be the most common occurrence) claims that a particular homework exercise was a “Common Core exercise”, which of course it cannot be since the CCSS are, as the name indicates, purely a set of standards to attain, not in any way a curriculum or curriculum content.

More generally, in fact, pretty well all critiques of the CCSS are due to a complete misunderstanding of what they are, why they are, and what they say. The issue was nicely dealt with in this 2014 article in the Hechinger Report.

My reason for bringing the Common Core into my series of posts was to point out that the standards were developed precisely to help guide school districts, schools, and teachers in the tricky task of updating K-12 mathematics education to adequately prepare tomorrow’s citizens for life and work in a world where calculation is no longer a central pillar of mathematics.

Having said that, I should point out that the above statement in no way implies that we should drop the teaching of basic arithmetic and algebra from the school system. As I discussed in some length in the third of my Nueva-inspired articles, the change that is required in K-12 math education is not so much in the mathematical topics but the reason they are now being taught and, in consequence, the way they should be taught.

Teaching for execution is no longer the primary driver, since no one using mathematics in the real world does that anymore. What is now of crucial importance is teaching for understanding. Digital systems outperform humans to an insane degree when it comes to execution. But they don’t understand; people have to supply that.

I leave you with an image I pulled from one of those Common Core social media rants some time ago. (I no longer remember the exact source.)

Typical social media posts about Common Core mathematics.
I have three comments about the post on the left. First, the mathematics in the bottom left is not some fancy new algorithm, it is what a child wrote down in reasoning (sensibly) about a particular arithmetic problem. Second, if you are unable to follow what the child is doing, you would have trouble making effective use of mathematics in today’s world. It’s pretty basic. (Your kid just did it, right?) Third, if you are a parent and you don’t see why it is important that today’s school students acquire those math reasoning skills, please don’t communicate your skepticism to your children. Doing so would be a great disservice, to your child, to your child’s math teacher, and to society. The mathematical world has changed significantly. That occurred over twenty years ago. It is not going to change back. Sit back, relax, be encouraging, and let the kids take over. They do just fine with it.

REFERENCE: During the period when the computer revolutionized how mathematics is done, I edited the American Mathematical Society’s “Computers and Mathematics” section of their monthly notices publication, sent to all members. I wrote about the column and that period in general in a paper that I submitted to the Proceedings of the Jon Borwein Commemorative Conference, held in 2017. Borwein, who died tragically young in 2016, was a leading pioneer in bringing digital technologies into mathematics. You can access a preprint of the paper HERE.

Wednesday, April 4, 2018

How today’s pros solve math problems: Part 3 (The Nueva School course)

By Keith Devlin

At the end of last month’s post, I left readers with a (seemingly) simple arithmetic problem. I prefaced the problem with the following two instructions:

2. Then, and only then, reflect on your answer, and how you got it.

The goal here, I said, is not to get the right answer, though a great many of you will. Rather, the issue is how do our minds work, and how can we make our thinking more effective in a world where machines execute all the mathematical procedures for us?

Here is the problem.

PROBLEM: A bat and a ball cost \$1.10. The bat costs \$1 more than the ball. How much does the ball cost on its own? (There is no special pricing deal.)

What answer did you get? And what did you learn from the subsequent reflection?

Before I continue, I should note that the use of this problem (which you can find in many puzzle books and on countless websites) in the context of trying to maximize the human mind’s innate abilities in order to become good 21st Century mathematical thinkers, is due to Gary Antonick, with whom I co-taught a Stanford Continuing Studies adult education course last fall. It was in that course that I gave the second iteration of the UPS problem I subsequently based my Nueva School course on. The discussion of the bat-and-ball problem that follows is the one Antonick presented in our course.

Now to the problem itself. The most common answer people give instantly to this problem is that the ball costs 10¢. It’s wrong (and many realize that is the case soon after their mind has jumped to that wrong number). What leads many astray is that the problem is carefully worded to run afoul of what under normal circumstances is an excellent strategy. (So if you got it wrong, you probably did so because you are a good thinker with some well-developed problem-solving strategies— problem-solving heuristics is the official term, and I’ll get to those momentarily. So take heart. You are well placed to do just fine in 21st Century mathematical thinking. You simply need to develop your heuristics to the next level.)

Here is, most likely, what your mind did to get to that 10¢ answer. As you read through the problem statement and came to that key phrase “cost more,” your mind said, “I will need to subtract.” You then took note of the data: those two figures \$1.10 and \$1. So, without hesitation, you subtracted \$1 from \$1.10 (the smaller from the larger, since you knew the answer has to be positive), getting 10¢.
Notice you did not really perform any calculation. The numbers are particularly simple ones. Almost certainly, you retrieved from memory the fact that if you take a dollar from a dollar-ten, you are left with 10¢. You might even have visualized those amounts of money in your hand.

Notice too that you understood the mathematical concepts involved. Indeed, that was why the wording of the problem led you astray!

What you did is apply a heuristic you have acquired over many financial transactions and most likely a substantial number of arithmetic quiz questions in elementary school. In fact, the timed tests in schools actively encourage such a “pattern recognition” approach. For the simple reason that it is fast and usually works!

We can, therefore, formulate a hypothesis as to why you “solved’ the problem the way you did. You had developed a heuristic (identify the arithmetic operation involved and then plug in the data) that is (a) fast, (b) requires no effort, and (c) usually works. This approach is a smart one in that it uses something the human brain is remarkably good at—pattern recognition—and avoids something our minds find difficult and requiring effort to master (namely, arithmetic calculation).

Of course, primed by the context in which I presented this particular problem, you probably expected there to be a catch. So, after letting your mind jump to the 10¢ answer, you likely took a second stab at it (or, if you were anxious about “getting a wrong answer,” made this your first solution) by applying an algorithm you had learned at school. Namely, you reasoned as follows:

Let x = cost of bat and y = cost of the ball. Then, we can translate the problem into symbolic
form as x + y = 1.10 ,   x = y + 1

Eliminate x from the two equations by algebra, to give
1.10 – y = y + 1

Transform this by algebra to give
0.10 = 2y

Thus, dividing both sides by 2, you conclude that
y = 5¢.

And this time, you get the correct answer.

You may, in fact, have been able to carry out this procedure in your head. When I was at school, I could do algebraic manipulations far more complicated than this in my head, at speed. But, truth be told, since I started outsourcing arithmetic to machines many decades ago, I have lost that skill, and now have to write down the equations and solve them on paper. (This is a confirmation, if any were needed, that arithmetic calculations do not come naturally to the human brain. Over the years, as my mental arithmetic skills have declined, my pattern recognition abilities have not diminished, but on the contrary have dramatically improved, as I learned—automatically, through exposure—to recognize ever more fine-grained distinctions.)

Whether or not you can do the calculation in your head, it is of course entirely formulaic and routine. Unlike the first method I looked at (a heuristic that is fast and usually right), this method is an algorithmic procedure, it is slow (much slower than the first method, even when the algebraic reasoning is carried out in your head), but it always works. It is also an approach that can be executed by a machine. True, for such a simple example, it’s quicker to do it by hand on the back of an envelope, but as a general rule, it makes no sense to waste the time of a human brain following an algorithmic procedure, not least because, even with simple examples it is familiarly easy to make a small error that leads to an incorrect answer.

But there is another way to solve the problem. It’s the way I addressed it, and, according to Antonick, who has given it to many professional mathematicians and asked them to vocalize their solutions, the way many math pros solve it. Like the first method we looked at, it is a heuristic, hence instinctive and fast, but unlike the first heuristic method, it always works.

This third method requires looking beyond the words, and beyond the symbols in the case of a problem presented symbolically, to the quantities represented. Though I (and likely other mathematicians) don’t visualize it quite this way (in my case it is more of a vague sense-of-size), the following image captures what we do.

As we read the problem, we form a mental sense of the two quantities, the cost of the ball-on-its-own and the cost of the bat-plus-ball, together with the stated relation between them, namely that the latter is \$1 more than the former. From that mental image, where we see the \$1.10 total consists of three pieces, one of which has size \$1 and the other two of which are equal, we simply “read off” the fact that the ball costs 5¢. No calculation, no algorithm. Pure pattern recognition.

This solution is an example of Number Sense, the critical 21st Century arithmetic skill I wrote about in the January 1, 2017 Huffington Post companion piece to the article I published on the same day as my article about all my math skills becoming obsolete, which I referred to in my last post here on Devlin’s Angle.

It is, I suggest, hard to imagine how a computer system could solve the problem that way. (Of course, you could write a program so it can perform that particular pattern recognition, but the essence of number sense is that you can apply it to many numerical problems you come across.)

Those three ways to solve the bat-and-ball problem I just outlined are examples of what the famous Australian (pure) mathematician Terrence Tao has called (in his blog), respectively, pre-rigorous thinking, rigorous thinking, and post-rigorous thinking. You can also listen to him explain these three categories in a short video in the Numberphile series.

Post-rigorous heuristic thinking is how today’s math pros use mathematics to solve real-world problems. In fact, as Tao makes clear, post-rigorous thinking is what the pros use most of the time to solve abstract problems in pure math. The formal, symbolic, rigorous stuff comes primarily at the end, to check that the solution is logically correct, or at various intermediate points to make those checks along the way.

In the case of solving real-world problems, the pros almost always turn to technology to handle any algebraic deductions. In contrast, though pure mathematicians sometimes do use those technology products as well, they often find it much quicker, and perhaps more fruitful in terms of gaining key insights, to do the algebraic work by hand.

So, one of the big question facing math teachers today is, how do we best teach students to be good post-rigorous mathematical thinkers?

In the days when the only way to acquire the ability to use mathematics to solve real-world problems involved mastering a wide range of algorithmic procedures, becoming a mathematical problem solver frequently resulted in becoming a post-rigorous thinker automatically.

But with the range of tools available to us today, there is a good reason to assume that, with the right kinds of educational experiences, we can significantly shorten (though almost certainly not eliminate) the learning path from pre-rigorous, through rigorous thinking, to post-rigorous mathematical thinking. The goal is for learners to acquire enough effective heuristics.

To a considerable extent, those heuristics are not about “doing math” as such. Rather, they are focused on making efficient and effective use of the many sources of information available to us today. But before you throw away your university-level textbooks, you need to be aware that the intermediate step of mastering some degree of rigorous thinking is likely to be essential. Post-rigorous thinking is almost certainly something that emerges from repeated practice at rigorous thinking. Any increased efficiency in the education process will undoubtedly come from teaching the formal methods in a manner optimized for understanding, as opposed to optimized for attaining procedural efficiency, as it was in the days when we had to do everything by hand. See Daniel Willingham’s excellent book Why Don’t Students Like School? for a good, classroom-oriented look at what it takes to achieve mastery in a discipline.

Now to that UPS routing problem that was the focus of my Nueva School course. [You will find it discussed here.] Here are some of the hints and suggestions about solving the problem I made to the students in the three courses where I used it. Whether they followed my advice was entirely up to them. The purpose of the course was not to solve the problem unaided—even an entire semester would not be enough time for that with students who had never approached a problem the way the pros do. Rather, it was to give them an experience of the method.

First, they had to work in teams of three to five. I let them select the teams, but said it would be good if at least one person on each team felt they were “good at math.”

Then, start out by using Google to find out what you can about the problem domain, and any attempts made by others to solve it.

Whenever you come across a reference to a concept, an approach, or a method that you suspect might be relevant, use general Web resources like Wikipedia to get an initial understanding of what they are and what they can do.

Follow any leads your search brings up to solutions of problems that look similar. Note what methods were used to solve them.

If you come across references to others who have worked on the problem, or a similar one, send them a brief email. You may not get a reply, but occasionally you will, and it could be invaluable. (I receive such emails all the time. Mostly I do not have time to respond, but occasionally one lands in my inbox when I have a spare moment, and I happen to know something that might help, so I shoot back a brief reply, often just a reference to a particular source.)

When you get to a point where you need to perform a specific calculation, perhaps because you have found a solution to a very similar problem someone else has obtained and published, but your data is different, use Wolfram Alpha. It is structured so you can use pattern recognition (of formulas) to identify the appropriate technique and then edit the example provided to be the one you want to solve.

Reinforce your use of Wolfram Alpha by using YouTube to find suitable videos that provide you with quick tutorials on the technique.

The resources I just mentioned are all listed on that chart of “Important Mathematical Technology Tools” I published with the first two articles in this series.

As it turns out, with the UPS routing problem, the sequence of steps I have outlined so far quickly leads to identification of a small number of possible solution techniques for which there are many very accessible YouTube videos, and in fact, for this problem there is no need to go much further into my list of tools, if at all.

You should, though, check out the various other resources on my list, to see what they offer. Each new problem has to be approached afresh, in its own terms. Twitter is on my list because it is my list, and I have sufficiently many math-expert Twitter followers that a quick tweet can often yield just the information I need, saving me having to send out a slew of emails to people I think might be able to help. LinkedIn is also idiosyncratic to me, since I have a good network of mathematics and technology professionals I can contact. But the other resources are pretty generic.

Ideally, everything goes much more smoothly if you can avail yourself the services of a math consultant to assist you in negotiating the various resources. (I was that consultant to the teams in the three courses I gave.)

Interestingly, in the final meeting of my Princeton class (which was the fist time I used the UPS problem in a course), after having the student teams present their solutions, I gave the solution I had obtained, at the end of which two individuals came up to me to say they hoped I had not minded their sitting in on the class. (It was an experimental course, and there had been strangers sitting in for one or two sessions throughout the semester, so I had not paid them any attention.) They were, they said, postdocs working with Professor X, who was a math consultant for UPS and had worked on the algorithm the class and I had been trying to reverse engineer. Hence their curiosity-driven attendance on the last day! Unbeknownst to me, my final lecture had been my oral exam!

“How did I do?”, I asked. “You got it pretty well right,” they replied.

Which was nice for me, but it would not have mattered if I had followed a different track. What was important from an educational standpoint was the process.

Something else I suggested to the class was to come up with a solution—any solution—as soon as you can. “Don’t worry if it is optimal or even right,” I said. “Just check it by computation, perhaps in the form of a spreadsheet simulation. Once you have some solution that you can check (in the case of my UPS problem, check against the shipping data I supplied, or any other UPS data you can find on the Web), you can iterate to find a better one. It might turn out that your first solution, or your first three or four, won’t even get you to first base, but in the process of formulating and checking those initial attempts, you will inevitably gain insight into the problem you are trying to solve. Remember, computation is cheap, fast, and essentially limitless.”

If you are not familiar with this way of solving math problems, it may not seem like an approach that will work. But it does. It is, in fact, how all of today’s pros do it!

If you have not already done so, now is a good time to check out the dictionary definition of the word heuristic! Here is Wikipedia’s (at the time of writing):

“A heuristic technique (from the ancient Greek for “find” or “discover”), often called simply a heuristic, is an approach to any problem-solving, learning, or discovery that employs a practical method not guaranteed to be optimal or perfect, but sufficient for the immediate goals. Where finding an optimal solution is impossible or impractical, heuristic methods can be used to speed up the process of finding a satisfactory solution. Heuristics can be mental shortcuts that ease the cognitive load of making a decision. Examples of this method include using a rule of thumb, an educated guess, an intuitive judgment, guesstimate, stereotyping, profiling, or common sense.”

Without an expert consultant, the heuristics approach to solving real-world problems can work, but it definitely goes a lot faster, and with a far great likelihood of success, if you have a math expert you can call on. Not to “do any math.” Computer systems handle those parts. Rather to help you negotiate the vast array of resources at your disposal and select the most promising one(s) to try next. For that is what using mathematics to solve a real-world problem really boils down to these days: managing resources.

And managing resources is something humans are innately good at. Natural selection always favors those creatures which are best able to manage the available resources. We are here as present-day humans because as a species we are good at doing that. What is new in the case of mathematical problem solving is that pieces of mathematics (formulas, equations, procedures, algorithms, techniques) are now among the “intellectual Lego pieces” (freely accessible on the Web) we can use as we assemble a solution.

As the students in my three courses could, in principle, attest, you don’t need vast expertise in mathematics to work this way. You just need to be a good thinker able to work in a small team. I say, “in principle,” since I think it highly likely most of not all the students felt they did not do much at all by way of using math to solve a problem. But that, I would say, is because they have a conception of “using math to solve a problem” rooted in the Nineteenth Century, if not the Fourth Century BCE. From my perspective, they absolutely were able to do what I just said they did.

Of course, they were not as good at it as I am. I’ve been at this game a lot longer, and, make no mistake about it, experience counts. (I think it is close to being the only thing that counts.)

What does not count, at least to any extent even remotely approaching the prohibitive degree it used to, is the ability to “do the math.” You just need to be able to select (hopefully, with help from someone with experience) the right pieces from the available online mathematical smorgasbord, and stitch them together in an appropriate way.

This kind of problem-solving doesn’t feel like math (as we all came to love or hate), that’s for sure. In fact, it doesn’t even feel like work. Once they got into the swing of it, even the students who declared they were not good at math or did not enjoy it, found they were having a good time, working in teams in a creative, explorative way. For the fact is, properly approached, humans enjoy problem-solving. (That’s another consequence of natural selection— problem-solving, particularly group problem solving, is one of our species’ key survival advantages.)

In fact, another way to look at the recent revolution in how we “use math” to solve real-world problems, is that it has brought “using math” into the mainstream of human group activities we naturally find enjoyable. At heart, mathematical thinking is little more than formalized common sense. It always has been. Which means it is something we can all do. (In my 2000 book The Math Gene, I presented an evolutionary explanation for the human brain’s acquisition of the ability to do mathematics, which implied that mathematical capacity is in the human gene pool, and hence available for all of us to “switch on.”) What caused many people problems over the centuries was that, before we had technologies that could handle the formal symbol-manipulation stuff, the only way to employ our innate capacity for mathematical thinking was to train the brain to do those manipulations. But manipulating algebraic symbols with logical precision is most definitely not something our brains evolved to do. (Our early ancestors’ lives on the savannas did not present much by way of a need for algebra.) So we find it very hard. Only with great effort over several years can we train our brains to do such work. And even then, we are error-prone.

Incidentally, practically everything I have said in this article applies to the way 21st Century coders work. In coding as in mathematics, the days are long gone when it was all about writing thousands of lines of instructions. The modern-day mathematician’s Web resource MathOverflow (on my chart of useful math tools) was modeled on, and named after, the coding world’s StackOverflow. Both groups of professionals use heuristics. In today’s world, highly regarded math problem solvers and good coders have simply acquired a richer and more effective set of heuristics than the ones who are less highly ranked. And for the most part, developing heuristics is a result of reflective experience, not some innate talent.

And there you have it. The primary goal in 21st Century mathematics-education-for-all is the development of a good repertoire of heuristics.

I’ll leave you with a graphical summary of Tao’s categorization of the three kinds of mathematical thinking we can bring to problem-solving. I introduced this categorization above to provide a perspective on the three phases each one of us has to go through to become proficient mathematical (real-world) problem solvers. But it also provides an excellent summary of three historical stages of mathematical thinking as it has evolved over the past ten thousand years or so, from the invention of numbers in Sumeria, where the mathematical thinking of the time was accessible to all, through three millennia of formal mathematics development, where many people were never able to make effective use of it, and now into the third phase, where, because of technology, mathematical thinking can once again be accessible to all.

To be sure, we do not know the degree to which people have to master rigorous thinking to become good post-rigorous thinkers. As I already noted, I don’t for a second imagine that stage can be by-passed. (See the Willingham book I cited.) But, given today’s technological toolkit, including search, social media, online resources like Wolfram Alpha and Khan Academy, and a wide array of online courses, it is absolutely possible to master most of the rigorous thinking you need “on the job,” in the course of working on meaningful, and hence motivational and rewarding, real-world problems.

This is not to say there is no further need for teachers. Far from it. Very few people are able to become good mathematical thinkers on their own. Newtons and Ramanujans, who achieved great things with just a few books, are extremely rare. The vast majority of us need the guidance and feedback of a good teacher.

What the inevitable transition to 21st Century math learning requires is that mathematics teachers operate very differently than in the past. The days where you need a live person to deliver information are largely over. Today, teaching is much more a matter of being a coach and mentor. To be sure, you can occasionally find such teaching on the Internet, but it works only if you can be one-on-one with that teacher. I expect there will be change, but I don’t expect an economy of scale. If I had to make a guess, I would predict that in due course you will find your (specialist) math teacher by going online to a Math-Teacher-Match.com website, where you will be paired with a practicing 21st Century math professional who spends part of each day coaching and mentoring students.

LABELS: mathematical thinking, problem-solving, rigorous thinking, pre-rigorous thinking, post-rigorous thinking, Terrence Tao, social media in mathematics