Thursday, May 7, 2015

Time to re-read (or read) What’s Math Got To Do With It?

Back in June 2010, I wrote a post to this blog in which I summarized a new book on K-12 mathematics teaching by a former Stanford colleague of mine, Prof Jo Boaler. At the time, though I had met Jo a few times, I did not really know her; rather I was just one of many mathematical educators who simply admired her work, some of which she described in the book What's Math Got To Do With It?, parts of which were the primary focus of my post.

Not long after my post appeared, Jo returned to Stanford from the UK, and over time we got to know each other better. When I formed my mathematics educational technology company BrainQuake in 2012, I asked her to be a founding member of its Board of Academic Advisors, all of whom are listed here. When she was putting the final touches to the new edition of her book, just published, she asked me to write a cover-quote, which I was pleased to do.

I say all of this by way of disclosure.* For my primary aim in writing this month’s column is to persuade you to read (or re-read) my earlier post, and ideally Jo’s book. The research findings she describes in the book highlight the lasting damage done to generations of K-12 students (and possibly consequent damage to the US economy when that generation of students enters the workforce) by continuing adherence to a classroom mathematics pedagogy that portrays math as a rule-based process of answer-getting, rather than a creative enterprise of understanding and problem solving.

The woefully ill-informed “debate” about the benefits of the US Common Core State Mathematics Standards that has been fostered in between the appearances of the two editions of Boaler’s book, make her message even more important than it was when the first edition came out in 2009. While CCSS opponents espouse opinions, Boaler presents evidence – lots of it – that supports the approach to K-12 mathematics learning the CCSS promotes.

If you want to see more of Prof Boaler’s efforts to improve K-12 mathematics education, see her teachers’ resource site YouCubed, or sign up for her online course How to Learn Math: for Teachers and Parents, which starts on June 16.

Also, check out her latest post in The Hechinger Report where she presents some recent data about the problems caused by a lot of old-style rule-memorization math instruction.

* NOTE: Prof Boaler’s Stanford research team also recently completed a small pilot study of BrainQuake’s mathematics learning (free-) app Wuzzit Trouble, first reported by education technology journalist Jordan Shapiro in an April 27, 2015 article in Forbes Magazine. (Prof Boaler is an academic advisor to BrainQuake but does so as a volunteer, and has no financial stake in the company.)

Wednesday, April 1, 2015

The Importance of Mathematics Courses in Computer Science Education

The confluence of two events recently reminded me of an article I wrote back in 2003 about the role of mathematics courses in university computer science education. [Why universities require computer science students to take math, Communications of the Association for Computing Machinery, Vol 46, No 9, Sept 2003, pp.36-39.]

The first event was a request for me to be an advisor on a research project to develop K-12 computer science programs. The second was a forum discussion in my Mathematical Thinking MOOC, currently in the middle of its sixth session.

My MOOC attracts a lot of mid-career computer professionals, who bring a different perspective to some of the issues the course considers. The forum thread in question focused on what is meant by a statement of the form “Let x be such that P(x).“ In mathematics, use of this statement requires that there exists an object satisfying P. If the existence is not known, you should express the statement counterfactually, as “Let x be an object such that P(x), assuming such an object exists.”

Some of the computer scientists, however, instinctively interpreted the statement “Let x be such that P(x)” as a variable declaration. This led them to give an “incorrect” answer to a question that asked then to identify exactly where the logic of a particular mathematical argument broke down. The logic failed with the selection of an object x that was not known to exist. In contrast, those computer scientists felt that things went wrong when the argument subsequently tried to do something with that x. That, they observed in the discussion, was where the program would fail.

It was a good discussion, that highlighted the distinction between the currently accepted view of mathematics as primarily about properties and relations, and the pre-nineteenth century view that it was at heart procedural. As such, it served as a reminder of the value of mathematics courses in computer science education, and vice versa.

The remainder of this post is what I wrote in the CACM back in 2003 (very lightly edited). I still agree with what I wrote then. (That is by no means always the case when I look at things I wrote more than a decade earlier.) I suspect that now, as then, some will not agree with me. (I actually received some ferociously angry responses to my piece.) Here goes.

Some years ago, I gave a lecture to the Computer Science Department at the University of Leeds in England. Knowing my background in mathematics — in particular, mathematical logic — the audience expected that my talk would be fairly mathematical, and on that particular occasion they were right. As I glanced at the announcement of my talk posted outside the lecture room, I noticed that someone had added some rather telling graffiti. In front of the familiar header “Abstract” above the description of my talk, the individual had scrawled the word “Very”.

It was a cute addition. But it struck me then, and does still, many years later, that it spoke volumes about the way many CS students view the subject. To the graffiti writer, operating systems, computer programs, and databases were (I assumed) not abstract, they were real. Mathematical objects, in contrast, so the graffiti-writer likely believed — and I have talked to many students who feel this way — are truly abstract, and reasoning about them is an abstract mental pursuit. Which goes to show just how good we humans are (perhaps also how effective university professors are) at convincing ourselves (and our students) that certain abstractions are somehow real.

For the truth is, of course, that computer science is entirely about abstractions. The devices we call computers don’t, in of themselves, compute. As electrical devices, if they can be said to do anything, it’s physics. It is only by virtue of the way we design those electrical circuits that, when the current flows, obeying the laws of physics, we human observers can pretend that they are doing reasoning (following the laws of logic), performing a numerical calculation (following the laws of arithmetic), or searching for information. True, it’s a highly effective pretence. But just because it’s useful does not make it any less a pretence.

Once you realize that computing is all about constructing, manipulating, and reasoning about abstractions, it becomes clear that an important prerequisite for writing (good) computer programs is an ability to handle abstractions in a precise manner. Now that, as it happens, is something that we humans have been doing successfully for over three thousand years. We call it mathematics.

This suggests that learning and doing mathematics might play an important role in educating future computer professionals. But if so, then what mathematics? From an educational point of view, in order to develop the ability to reason about formal abstractions, it is largely irrelevant exactly what abstractions are used. Our minds, which evolved over tens of thousands of years to reason (largely imprecisely) about the physical world, and more recently the social world, find it extremely difficult accepting formal abstractions. But once we have learned how to reason precisely about one set of abstractions, it takes relatively little extra effort to reason about any other.

But surely, you might say, even if I’m right, when it comes to training computer scientists, it makes sense to design educational courses around the abstractions the computer scientists will actually use when they graduate and go out to work in the technology field. Maybe so (in fact no, but I’ll leave that argument to another time), but who can say what the dominant programming paradigms and languages will be four years into the future? Computing is a rapidly shifting sand. Mathematics, in contrast, has a long history. It is stable and well tested.

Sure, there is a good argument to be made for computer science students to study discrete mathematics rather than calculus. But, while agreeing with that viewpoint, I believe it is often overplayed. Here’s why I think this.

A common view of education is that it is about acquiring knowledge — learning facts. After all, for the most part that is how we measure the effectiveness of education: by testing the students’ knowledge. But that’s simply not right. It might be the aim of certain courses, but it’s definitely not the purpose of education. The real goal of education is to improve minds — to enable them to acquire abilities and skills to do things they could not do previously. As William Butler Yeats put it, “Education is not about filling a bucket; it’s lighting a fire.” Books and USB memory sticks store many more facts than people do — they are excellent buckets — but that doesn’t make them smart. Being smart is about doing, not knowing.

Numerous studies have shown that if you test university students just a few months after they have completed a course, they will have forgotten most of the facts they had learned, even if they passed the final exam with flying colors. But that doesn’t mean the course wasn’t a success. The human brain adapts to intellectual challenges by forging and strengthening new neural pathways, and those new pathways remain long after the “facts” used to develop them have faded away. The facts fade, but the abilities remain.

If you want to prepare people to design, build, and reason about formal abstractions, including computer software, the best approach surely is to look for the most challenging mental exercises that force the brain to master abstract entities — entities that are purely abstract, and which cause the brain the maximum difficulty to handle. And where do you find this excellent mental training ground? In mathematics.

Software engineers may well never apply any of the specific theorems or techniques they were forced to learn as students (though some surely will, given the way mathematics connects into most walks of life in one way or another). But that doesn’t mean that those math courses were not important. On the contrary. The main benefit of learning and doing mathematics, I would argue, is not the specific content; rather it’s the fact that it develops the ability to reason precisely and analytically about formally defined abstract structures.

Monday, March 9, 2015

Pi Day, Cyclical Motion, and a Great Video Explanation of Multiplication


March 14 is Pi Day, the day in the year when we celebrate the world’s most famous mathematical constant.

Back in 1988, on March 14, a physicist called Larry Shaw organized the first Pi Day celebration at the Exploratorium in San Francisco, where he worked. It was meant to be just a one-off, fun event to get kids interested in math. Children were invited in to march around one of the Exploratorium’s circular rooms and end up eating fruit pies. But the idea took off, and ever since, March 14 has been Pi Day. Not just at the Exploratorium, but with celebratory events organized all across the United States, and in other parts of the world.

In case you haven’t twigged it, we celebrate Pi Day on March 14 because, in American date format, that day is 3.14, which is pi to two decimal places.

This year is a particularly special, once-in-a-century Pi Day, since the American format date this year is 3.14.15, pi to four decimal places. If you want more pi-accuracy, drink a toast to pi at time 9:26:53 (AM or PM), to get the first nine places 3.141592653.

That degree of accuracy, by the way, is more than enough for practical purposes. If you use that value to calculate the circumference of the Earth, the answer will be accurate to within 1/4 inch.

Though we have known since the 18th Century that pi is irrational (indeed, transcendental, thereby demonstrating that you cannot square a circle), calculating approximate values of pi has a long history. In ancient times, Babylonians, Egyptians, Greeks, Indians, and Chinese mathematicians calculated the first three or four places, and found fraction approximations like 22/7 and 355/113.

In the 16th century, a German who presumably had a lot of time on his hands spent most of his life computing pi to 36 places, and a 19th century American went all the way to 707 places, but he mad a mistake after 527 places, so the last part of his answer was wrong.

In more recent times, computers have been used to compute pi to well over a trillion places, in part for sport, but also to test the accuracy of high speed supercomputers.

Of course, PI Day isn’t really just about pi, it’s an excuse to celebrate all of mathematics, and in particular stimulate interest in mathematics among children and young adults. You will find Pi Day events in schools and colleges, at science museums, and other venues. Teachers, instructors, and students organize all kinds of math-related events and competitions. The value of pi simply sets the date.

With this year’s special Century edition, some large organizations are putting on celebratory events, among them the Museum of Mathematics in New York City (details of the event here), the Computer History Museum just south of San Francisco (details here), and the NASA Space Center in Houston (see here). And at the big Teaching and Learning Conference in Washington D.C. this week, I’m hosting a Pi Celebration at 8:00AM on Saturday morning.

There are many other celebrations. Check to see what is going on in your area. If there is a large science or technology organization nearby, they may well be putting on a Pi Day event.

The media have been getting in on the act too. NPR will air one of my short Math Guy conversations with Weekend Edition host Scott Simon this Saturday morning, and today’s New York Times ran a substantial article about pi by their regular Numberplay contributor Garry Antonick.

Antonick led off with a short pi-related problem I provided him with, and in honor of the Pi Day of the Century, in place of the traditional photo of me at a blackboard, he picked an action shot of me cresting a mountain on a bicycle (pi motion if ever there were) in a Century (100 mile) ride back in 2013.

He could not resist bringing in the famous Euler Identity, linking the five most significant constants of mathematics, pi, e, i, 0, and 1. This has always been my favorite mathematical identity, and Antonick quotes from a magazine article I wrote about it a few years ago.

But truth be told, it is not my favorite pi fact. For the simple reason, it’s not really about pi, rather it is about multiplication and exponentiation. Pi gets in because both operations involve the number.

My favorite pi fact, ever since I first came across it as a teenager (one of several eye-opening moments that motivated me to become a mathematician), is Leibniz’s series (sometimes called Gregory’s series), which dates from the 17th century. You write down an endless addition sum that starts out 1/1, minus 1/3, plus 1/5, minus 1/7, plus 1/9, etc. All the reciprocals of the odd numbers, with alternating signs.

Since this sum goes on for ever, you can’t actually add it up term by term, but you can use mathematical techniques to determine the answer a different way. And that answer is pi/4.

What does pi have to do with adding the reciprocals of the odd numbers? As with Euler’s Identity, Leibniz’s series provides a glimpse of the deep structure of numbers and arithmetic that lies just beneath the surface.

Talking of which, I caused a huge stir a few years ago when I ran a series of Devlin’s Angle posts trying to rid people (in particular, math teachers) of their false (and educationally dangerous) belief that multiplication is repeated addition (and exponentiation is repeated multiplication).

The initial series ran in June, July-August, and September 2008. When the barrage of facts I referenced in the third of those posts failed to stem the flood of disbelieving reactions of readers, I ran a lengthy post in January 2011 trying to convey the truly deep (and powerful) structure of multiplication.

Still to little avail. Put repeated addition in the same bin as evolution by natural selection, climate change, and the Golden Ratio. For many people, no amount of facts can overturn a long held and cherished belief. It’s a common human trait – fortunately not a universal one, else we’d still be living in caves and mud huts. (A politician who says “I am not a scientist” is effectively saying “I don’t understand the difference between building my mansion and a mud hut.”)

Unfortunately, as a wordsmith, I did not, and do not, possess the skill to provide a really good explanation of multiplication. I had to resort to spinning a multi-faceted story based on scaling. Someone who does have what it takes to tell the story properly, using video, is Stanford mathematics and computer science senior undergraduate Grant Sanderson. His recent video on Euler’s Identity is the best explanation of addition and multiplication I have ever seen. Period. Antonick embeds it in his New York Times piece. It deserves widespread circulation.

The video actually goes on to discuss the exponential function, and then the Euler Identity, but I suspect many viewers will get lost at that point. The exponential function is pretty sophisticated. Much more so than addition and multiplication. In contrast, all it takes to understand those two staples of modern numerical life is to get beyond the ultimately misleading concepts many of us form in the first few years of our lives. Do that, and Sanderson’s video provides the rest.

As is so often said, a picture can be worth a thousand words. Sanderson demonstrates that a motion picture can be worth a hundred thousand.

NOTE: I did try song a few years ago, collaborating with a Santa Cruz choral group called Zambra. The result can be found here. There’s lots of pi stuff in those compositions. But it’s primarily musical interpretation of mathematics, not explanation. (For instance, check out our rendering of Leibniz’s series.)

Finally, I often get asked why we use the Greek letter pi to denote the ratio of the circumference of a circle to its perimeter of a circle to its diameter.. This convention goes back to the 18th Century.

Sunday, February 1, 2015

The Greatest Math Teacher Ever?

Last month I wrote about the kind of mathematic learning experiences we need to design to prepare young people for life in the Twenty-First Century. I cited the hugely successful, pioneering educational work of the late Professor R L Moore of the University of Texas. This follow up article about Moore and his teaching method is a combination of two earlier Devlin’s Angle posts, from May 1999 and June 1999. Other than adding a short paragraph at the end leading to further information about Moore, the only changes to my original text are minor updates to adjust for the passage of time.

The set-up

Each year, the MAA recognizes great university teachers of mathematics. Anybody who has been involved in selecting a colleague for a teaching award will know that it is an extremely difficult task. There is no universally agreed upon, ability-based linear ordering of mathematics teachers, even in a single state in a given year. How much more difficult it might be then to choose a “greatest ever university teacher of mathematics.” But if you base your decision on which university teacher of mathematics has had (through his or her teaching) the greatest impact on the field of mathematics, then there does seem to be an obvious winner. Who is it?

Most of us who have been in mathematics for over thirty years probably know the answer. Let me give you some clues as to the individual who, I am suggesting, could justifiably be described as the American university mathematician who, through his (it’s a man) teaching, has had the greatest impact on the field of mathematics.

He died in 1974 at the age of 91.

He was cut in the classic mold of the larger-than-life American hero: strong, athletic, fit, strikingly good looking, and married to a beautiful wife (in the familiar Hollywood sense).

He was well able to handle himself in a fist fight, an expert shot with a pistol, a lover of fast cars, self-confident and self-reliant, and fiercely independent.

Opinionated and fiercely strong-willed, he was forever embroiled in controversy.

He was extremely polite; for example, he would always stand up when a lady entered the room.

He was a pioneer in one of the most important branches of mathematics in the twentieth century.

He was a elected to membership of the National Academy of Science, as were three of his students.

The method of teaching he developed is now named after him.

If you measure teaching quality in terms of the product - the successful students - our man has no competition for the title of the greatest ever math teacher. During his 64 year career as a professor of mathematics, he supervised fifty successful doctoral students. Of those fifty Ph.D.’s, three went on to become presidents of the AMS - a position our man himself held at one point - and three others vice-presidents, and five became presidents of the MAA. Many more pursued highly successful careers in mathematics, achieving influential positions in the AMS and the MAA, producing successful Ph.D. students of their own and helping shape the development of American mathematics as it rose to its present-day position of world dominance.

In the first half of the Twentieth Century, fully 25% of the time the president of the MAA was either a student or a grandstudent of this man.

Other students and grandstudents of our mystery mathematician served as secretary, treasurer, or executive director of one of the two mathematical organizations and were editors of leading mathematical journals.

After reaching the age of 70, the official retirement age for professors at his university, in 1952, he not only continued to teach at the university, he continued to teach a maximum lecture load of five courses a year - more than most full-time junior faculty took on. He did so despite receiving only a half-time salary, the maximum that state regulations allowed for someone past retirement age who for special reasons was kept on in a “part-time” capacity.

He maintained that punishing schedule for a further eighteen years, producing 24 of his 50 successful Ph.D. students during that period.

He only gave up in 1969, when, after a long and bitter battle, he was quite literally forced to retire.

In 1967, the American Mathematical Monthly published the results of a national survey giving the average number of publications of doctorates in mathematics who graduated between 1950 and 1959. The three highest figures were 6.3 publications per doctorate from Tulane University, 5.44 from Harvard, and 4.96 from the University of Chicago. During that same period, our mathematician’s students averaged 7.1. What makes this figure the more remarkable is that our man had reached the official retirement age just after to the start of the period in question.

Who was he?

The answer

His name is Robert Lee Moore. Born in Dallas in 1882, R. L. Moore (as he was generally known) stamped his imprint on American mathematics to an extent that only in his later years could be fully appreciated.

As I noted earlier, during 64 year career, the last 49 of them at the University of Texas, Moore supervised fifty successful doctoral students. Three of them went on to become presidents of the AMS (R. L. Wilder, G. T. Whyburn, R H Bing) -- a position Moore also held -- and three others vice-presidents (E. E. Moise, R. D. Anderson, M. E. Rudin), and five became presidents of the MAA (R. D. Anderson, E. Moise, G. S. Young, R H Bing, R. L. Wilder). Many more pursued highly successful careers in mathematics, achieving influential positions in the AMS and the MAA, producing successful Ph.D. students of their own -- mathematical grandchildren of Moore -- and helping shape the development of American mathematics as it rose to its present-day dominant position. That’s quite a record!

In 1931 Moore was elected to membership of the National Academy of Science. Three of his students were also so honored: G. T. Whyburn in 1951, R. L. Wilder in 1963, and R H Bing in 1965.

In 1973, his school, the University of Texas at Austin, named its new, two-winged, seventeen-story mathematics, physics, and astronomy building after him: Robert Lee Moore Hall. This honor came just over a year before his death. The Center for American History at the University of Texas has established an entire collection devoted to the writings of Moore and his students.

Discovery learning

Moore was one of the founders of modern point set topology (or general topology), and his research work alone puts him among the very best of American mathematicians. But his real fame lies in his achievements as a teacher, particularly of graduate mathematics. He developed a method of teaching that became widely known as “the Moore Method”. Its present-day derivative is often referred to as “Discovery Learning” or “Inquiry-Based Learning” (IBL).

One of the first things that would have struck you if you had walked into one of Moore’s graduate classes was that there were no textbooks. On each student’s desk you would see the student’s own notebook and nothing else! To be accepted into Moore’s class, you had to commit not only not to buy a textbook, but also not so much as glance at any book, article, or note that might be relevant to the course. The only material you could consult were the notes you yourself made, either in class or when working on your own. And Moore meant alone! The students in Moore’s classes were forbidden from talking about anything in the course to one another -- or to anybody else -- outside of class. Moore’s idea was that the students should discover most of the material in the course themselves. The teacher’s job was to guide the student through the discovery process in a modern-day, mathematical version of the Socratic dialogue. (Of course, the students did learn from one another during the class sessions. But then Moore guided the entire process. Moreover, each student was expected to have attempted to prove all of the results that others might present.)

Moore’s method uses the axiomatic method as an instructional device. Moore would give the students the axioms a few at a time and let them deduce consequences. A typical Moore class might begin like this. Moore would ask one student to step up to the board to prove a result stated in the previous class or to give a counterexample to some earlier conjecture -- and very occasionally to formulate a new axiom to meet a previously identified need. Moore would generally begin by asking the weakest student to make the first attempt -- or at least the student who had hitherto contributed least to the class. The other students would be charged with pointing out any errors in the first student’s presentation.

Very often, the first student would be unable to provide a satisfactory answer -- or even any answer at all, and so Moore might ask for volunteers or else call upon the next weakest, then the next, and so on. Sometimes, no one would be able to provide a satisfactory answer. If that were the case, Moore might provide a hint or a suggestion, but nothing that would form a constitutive part of the eventual answer. Then again, he might simply dismiss the group and tell them to go away and think some more about the problem.

Moore’s discovery method was not designed for -- and probably will not work in -- a mathematics course which should survey a broad area or cover a large body of facts. And it would obviously need modification in an area of mathematics where the student needs a substantial background knowledge in order to begin. But there are areas of mathematics where, in the hands of the right teacher -- and possibly the right students -- Moore’s procedure can work just fine. Moore’s own area of general topology is just such an area. You can find elements of the Moore method being used in mathematics classes at many institutions today, particularly in graduate courses and in classes for upper-level undergraduate mathematics majors, but few instructors ever take the process to the lengths that Moore did, and when they try, they rarely meet with the same degree of success.

Part of the secret to Moore’s success with his method lay in the close attention he paid to his students. Former Moore student William Mahavier (now deceased) addressed this point:

“Moore treated different students differently and his classes varied depending on the caliber of his students. . . . Moore helped his students a lot but did it in such a way that they did not feel that the help detracted from the satisfaction they received from having solved a problem. He was a master at saying the right thing to the right student at the right time. Most of us would not consider devoting the time that Moore did to his classes. This is probably why so many people claim to have tried the Moore method without success.”

Another famous (now deceased) mathematician who advocated -- and has successfully used -- (a modern version of) the Moore method was Paul Halmos. He wrote:

“Can one learn mathematics by reading it? I am inclined to say no. Reading has an edge over listening because reading is more active -- but not much. Reading with pencil and paper on the side is very much better -- it is a big step in the right direction. The very best way to read a book, however, with, to be sure, pencil and paper on the side, is to keep the pencil busy on the paper and throw the book away.”

Of course, as Halmos went on to admit, such an extreme approach would be a recipe for disaster in today’s over-populated lecture halls. The educational environment in which we find ourselves these days would not allow another R. L. Moore to operate, even if such a person were to exist. Besides the much greater student numbers, the tenure and promotion system adopted in many (most?) present-day colleges and universities encourages a gentle and entertaining presentation of mush, and often punishes harshly (by expulsion from the profession) the individual who seeks to challenge and provoke -- an observation that is made problematic by its potential for invocation as a defense for genuinely poor teaching. But as numerous mathematics instructors have demonstrated, when adapted to today’s classroom, the Moore method -- discovery learning -- has a lot to offer.

If you want to learn more about R. L. Moore and his teaching method, check out the web site: http://legacyrlmoore.org/.

But before you give the method a try, take heed of the advice from those who have used it: Plan well in advance and be prepared to really get to know your students. Halmos put it this way:

“If you are a teacher and a possible convert to the Moore method ... don’t think that you’ll do less work that way. It takes me a couple of months of hard work to prepare for a Moore course. ... I have to chop the material into bite-sized pieces, I have to arrange it so that it becomes accessible, and I must visualize the course as a whole -- what can I hope that they will have learned when it’s over? As the course goes along, I must keep preparing for each meeting: to stay on top of what goes on in class. I myself must be able to prove everything. In class I must stay on my toes every second. ... I am convinced that the Moore method is the best way to teach there is -- but if you try it, don’t be surprised if it takes a lot out of you.”

There is a caveat

When I wrote those two Devlin’s Angle posts back in 1999, I debated with myself whether to address a side to the Moore story that, particularly from a late Twentieth Century perspective, does not stand to his credit. The issue is race.

Moore’s racial attitude was nothing unusual for a white person who was born and lived most of his life in Texas in the late Nineteenth Century and the first three quarters of the Twentieth. When the Civil Rights Act was passed in 1964 (yes, that recently!), making racial discrimination illegal, Moore was already long past retiring age, and just five years short of actually vacating his university office. Moreover, no one who regularly reads a newspaper would believe that racial discrimination in America is a thing of the past. Moore’s racial views are still not unusual in Texas and elsewhere.

Were Moore not such a towering figure, his position on race (at least as demonstrated by his actions) would not merit attention. But like all great people, all aspects of his life become matters of scrutiny. Moore could have acted differently when it came to race, even back then, in Texas, but he did not. And from today’s perspective, that inevitably leaves an uncomfortable stain on his legacy.

In writing my two 1999 columns, I chose to focus on Moore the university teacher, in particular to raise awareness of discovery learning in mathematics. The focus of Devlin’s Angle is, after all, mathematics and mathematics teaching. I did not want to distract from that goal with what is clearly a side issue, particularly such an explosive one. Moore’s larger-than-life character was clearly a significant part of his success. His racism (or at least racist behavior) was not a part of that success story – if for no other reason than because he never accepted any Black students. So I did not raise the issue.

For the same reason, I have left this side of the Moore story to the end here. We can learn from Moore when it comes to designing good mathematics learning experiences, and even admire him as a highly gifted teacher, without condoning other aspects of his life, just as we can enjoy Wagner’s music without endorsing Nazism. I can however leave you with a pointer to an article posted online by Mathematics Professor Scott Williams on 5/28/99, about the same time my articles appeared (and possibly in response to the first of them). Like it or not, Williams’ post shines light on another side to the Moore story. We can learn things from great people in ways other than taking a class from them, and we can perhaps learn things they were not trying to teach us.

References

Paul R. Halmos (1975), The Problem of Learning to Teach: The Teaching of Problem Solving, American Mathematical Monthly, Volume 82, pp.466-470.
Paul R. Halmos (1985), I Want to Be a Mathematician, Chapter 12 (How to Teach), New York: Springer-Verlag, pp.253-265.
William S. Mahavier (1998), What is the Moore Method?, The Legacy of R. L. Moore Project, Austin, Texas: The University of Texas archives.

Thursday, January 1, 2015

Your Father’s Mathematics Teaching No Longer Works

Gender-challenged title courtesy of this famous 1988 Oldsmobile TV commercial:


The start of a New Year is traditionally a time when we resolve to make changes. Change is particularly imperative in US mathematics education, which is built on a (Nineteenth Century) pedagogic model that long since passed its expiry date.

In a nutshell, the school system we all grew up with was essentially developed in Nineteenth Century Britain to provided a global infrastructure to run the British Empire. In modern terms, the British Imperialists created an “Internet” and an “Internet of Things” using the best computational and manufacturing resources available at the time: people.

Controlled by an “Internet of human computers”: The British Empire in 1922.
Map from trivto.deviantart.com.
While few of us in K-16 education today see it as merely a process to prepare young people for work, we inherited a system built to do just that.

Now we have an electronic, digital Internet, does it make sense to continue to use the old system?

What do Twenty-First Century citizens need from their education?

While not the only thing—not even close—equipping young people for work is still an important educational goal, both for the individual and for society as a whole. Accordingly, it makes sense for those of us in systemic education to be constantly aware of the skills that are actually required in the workplace. If those skills change, so should the education we provide.

A good place to start is by asking the leaders of the leading companies what they look for when hiring new employees. The table below shows us what skills the Fortune 500 companies were asking for in 1970, then again thirty years later in 1999.

Fortune 500 most valued skills, cited in
Linda Darling-Hammond et al, Criteria for High-Quality Assessment (2013)

It makes for dramatic reading. While the required skills ranked from 4 through 9 remained unaltered, the top three changed completely. The most important skill in 1970, writing, dropped to number 10, while skills two and three, computation and reading, respectively, dropped off the top ten list entirely.

The most important skill in the workplace at the start of the Twenty-First Century, according to those leading companies, is teamwork, which in a single generation had leapt up from number 10. The other two skills at the top, Problem Solving and Interpersonal Skills, were not even listed back in 1970.

Clearly, the world (of work) has changed, at least for those of us living in advanced societies. Unfortunately, for those of us in the United States, and many other parts of the world, our education system has failed to keep up.

In large part, this is because of the hard-to-avoid inertia that so often comes with national (or statewide) education systems. By and large, many politicians and bureaucrats are far less aware of rapidly changing workforce requirements than those in business, and politicians frequently pander to the often woefully uninformed beliefs of voters, who tend to resist change–especially change that will affect their children.

In the US, we see this dramatically illustrated by the widespread resistance to the Common Core State Standards. In the case of mathematics, just look at how closely the eight basic Mathematics Principles of the CCSS align to that Fortune 500 list of required Twenty-First Century skills:
  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.
The fact is, any parent who opposes adoption of the CCSS is, in effect, saying, “I do not want my child prepared for life in the Twenty-First Century.” They really are. Not out of lack of concern for their children, to be sure. Quite the contrary. Rather, what leads them astray is that they are not truly aware of how the huge shifts that have taken place in society over the last thirty years have impacted educational needs.

Having lived through those changes, parents have (for the most part) been able to build on their own education and cope with new demands. “What worked for me will work for my children,” they say. (They say that even when it patently did not work for them!)

But the situation is very different for their children. They are being thrust straight into that new world. To prepare them for that, you need a very different kind of education: one based on understanding rather then procedural mastery, and on exploration rather than instruction.

One of the best summaries of this societal change, and the resulting need for educational shift, that I know is the 22 minutes TED Talk Build a School in the Cloud, given by the educational researcher Sugata Mitra, winner of the 2013 TED Prize.

In his talk, not only does Mitra explain why we need to make radical changes to education, he provides examples, backed by solid evidence, of how a “Fortune 500 oriented,” team-based, exploratory approach works. In the late 1990s and throughout the 2000s, Mitra conducted experiments in which he gave children in India access to computers. Without any instruction, they were able to teach themselves a variety of things, from English to DNA replication.

[See also Mitra’s earlier talk from 2010.]

Another good account of this need for educational change is provided by Sir Ken Robinson, also in a TED Talk (11 minutes).

These ideas are not new. Indeed, they are mainstream in educational research circles. They just have not permeated society at large.

For instance, Harvard physicist Eric Mazur has been teaching by Inquiry-Based Learning (IBL), to use one of several names for this general approach, for over twenty-five years, since he first noticed that instructional lectures simply do not work. He describes his approach, and the reasons for adopting it, in his 2009 talk Confessions of a Converted Lecturer (18 minutes, abridged version).

In mathematics, the IBL approach goes way back to the 1920s. I wrote about the best known proponent in two Devlin’s Angle posts back in 1999: May, June.

Moore’s ideas have been adapted and used successfully in present-day mathematics classrooms, as shown in the promotional video Creativity in Mathematics: Inquiry-Based Learning and the Moore Method (20 minutes).

Lest my account of R L Moore and that last video portrayal leaves you with the impression that IBL math is for bright college students, see also this WIRED magazine account of the success Mexican teacher Sergio Juárez Correa had when he took a Mitra inspired approach into a poor school in Matamoros, a city of half-a-million known more for its drug trade than for being at the forefront of Twenty-First Century mathematics education.

Remember, Bob Dylan sang this in 1964:



AS OF TODAY, THAT’S OVER FIFTY YEARS AGO!

It’s long past time for the education system to catch up with the world outside the classroom. That should be our resolution for 2015.

Wednesday, December 10, 2014

How do you find good math learning apps?

There are approximately 20,000 math learning apps available on the App Store (classified as such by their creators). Google Play does not provide the corresponding figure for Android apps, but presumably there are a lot there as well.

Most of those apps do little more than provide repetitive practice of very basic skills, primarily about numbers. They are essentially just animated flash cards.

How can a parent, or a teacher, decide which apps are likely to benefit their child, or their students? I’ll come back to that later.

First, let me say that there is not necessarily anything wrong with an app that is essentially just an animated flash card – unless parents buy them (or just download them, as the majority are free) thinking that putting them on their children’s iPad or whatever is all they need to do to improve their performance in math.

In the days when the gateway to mathematics, and indeed much of everyday life, lay in mastering the multiplication tables and memorizing a few formulas for calculating areas and volumes, mastery of the basic number facts was indeed enough to start with. So it’s a pity those fun learning apps were not available back then. They would have made the acquisition of those fundamental facts and skills so much easier and far more enjoyable.

Unfortunately, the very digital technologies that have put those learning apps into eager young hands have also provided tools that have rendered procedural mastery of those basic skills all but irrelevant.

In today’s world, we use cheap, ubiquitous devices to do our calculations. It’s no longer important that all members of society have procedural mastery of basic arithmetic. What is required is the ability to make effective use of those digital devices, and what that depends upon is a good understanding of number – what is often referred to as number sense.

Roughly speaking, having number sense means being proficient with quantities and operations with numbers. A person with number sense is able to represent number concepts with models, words and diagrams, to communicate numerical ideas, and solve problems involving numbers. She or he can flexibly compose and decompose numbers for computation and solving problems. They can evaluate the reasonableness of solutions to numerical problems, and make connections between multiple solution methods. They can communicate their number sense verbally and in writing. They notice and explore number patterns, make connections and conjectures, and communicate their thinking to others. Number sense goes beyond solving word problems and memorizing basic facts and procedures. It involves engaging in numbers and operations in ways that develop a deep understanding of the content, which provides a firm foundation for mathematical success. In particular, a strong background in number sense sets the stage for later success in algebra and other parts of mathematics.

If that last paragraph sounds like something that emerged from a committee of mathematics education experts, it is because in essence it did. You find language like that in the National Research Council’s 2000 report Adding it Up, (which you can download for free from the National Academies Press) and in the preamble to the Common Core State Standards for Mathematics, which emphasize the development of number sense in young children.

For sure, you cannot have number sense without being able to solve an arithmetic problem and get the right answer. What has changed is that it is no longer important to solve that problem by the fastest method, or by a standard method that leaves a paper audit trail that others can check. Our calculating devices do those for us.

Much more important in today’s world is to be able to reason about the numbers in a problem from first principles, in a way that embodies the internal structure of the numbers. For as humans, we need to be able to operate when and where that calculator cannot: namely, when we are faced with a novel problem the real world has thrown up at us.

It was a lack of recognition that the world has changed fundamentally that led the consequently-Internet-famous “Jack’s Dad” to pen his satirical “letter to his son” that went viral on social media earlier this year. (See the next link below.)

Actually, Jack’s dad is an electronics engineer, so he was certainly aware of how much today’s world was different from the days of his own childhood. Unfortunately, as someone outside the world of education, he had just not connected the dots to understand what changes in education were required in order to properly prepare today’s kids to live, not just in our present world, but in the world they will help shape from it.

One of the best summaries of the issues behind that social media firestorm that I came across was the April 6 response to Jack’s Dad written by the math education blogger Christopher Danielson.

Danielson’s observations about different kinds of expertise rang very true to me. Having devoted the first part of my mathematics career to mathematical research, it was my appointment to serve on the Mathematical Sciences Education Board in 2000, and the close contact with leading experts in mathematics teaching that resulted, that brought home to me just how little I knew about how people learn mathematics, and how (consequently) we should teach it.

Put plainly, having a PhD in mathematics and a string of published research is absolutely nothing like enough background to speak with authority about K-12 mathematics learning. People like me can provide good advice on mathematical content; but not on mathematics teaching. That requires different knowledge and expertise.

My own university, Stanford, famous for its very high standards in research, apparently recognizes this when it comes to hiring new faculty in Education. While I cannot speak with authority for the School of Education’s policies, I have observed that no one gets appointed to the faculty who has not spent several years in K-12 teaching. (In addition to having done and published first class research!) Whether or not K-12 experience is official hiring policy, it certainly plays out that way, and it seems to me to be a sensible criterion to demand.

Going back to the standard algorithms and Jack’s Dad, a few months after his first post, on October 8, Danielson posted another excellent blog on the degree to which the position occupied by the standard arithmetic algorithms (in actual fact, there are many variations, so there is no such thing as “the standard algorithms) has changed in the educational landscape – from being the main focus as a method for daily use, to an interesting and historically important example of a set of highly efficient paper-and-pencil algorithms that quite literally changed the world. Their significance was a consequence of the dominant information storage and communication technology of the time: flat, static writing surfaces such as parchment, blackboards, and paper. (I describe that story in my book The Man of Numbers.)

I will note, in passing, that Danielson’s October post indicates that some math learning apps may in fact do harm to a child’s mathematics learning, an observation that should be coupled with my earlier remarks about choosing basic skills educational apps.

What put these thoughts onto my front burner recently were some discussions I was having with members of the Scientific Advisory Board for my educational technology startup company BrainQuake.

If you check out our company’s Team page, you will find we have recruited a number of world renown experts in mathematics education. Now you may think they are just there for marketing purposes – website name dropping. But you would be wrong. Each one is there because they bring very valuable, very specific expertise to the table.

To someone not an expert in mathematics learning, the arithmetic puzzles in our launch app, Wuzzit Trouble, may look as though they are just a series of problems we generated in an essentially random fashion, following the simple rule that the numbers should get "harder" the further a player goes in the game. But that is not the case. In a mathematics learning game, the mathematics ramp is just as critical as the level design of the game, and both require a lot of expertise to get it right.

(Interestingly, another name on our website, John Romero, is a world expert in level design – the ramping in game-play – but he joined forces with us only after we had brought out Wuzzit Trouble, so you will only see the results of his genius in future products we bring out.)

Which brings me back to my promise to provide advice on how to select good learning apps. It’s probably not a foolproof method, but a quick and easy way is to check out the website of the creators, and see who they have advising them on the learning side.

There is always the danger that some of the names are there for little more than window dressing, but the majority of education experts (indeed, experts in any domain) are not likely to lend their name to an enterprise they do not believe in. So the presence of names of distinguished mathematics educators should give you a lot of confidence in the product.

More to the point, the absence of such names should be taken as a serious warning. Quite frankly, it is not possible to design and build an educationally sound and effective learning app without a lot of expert input.

And I mean a lot of expert input. I bring years of my own expertise to BrainQuake, but Wuzzit Trouble would not have been anything like as educationally successful as it has, if it had just been me on the mathematics side.

There is your quick-and-easy quality check. If you use it, you will find that list of 20,000 apps suddenly shrinks down to a significantly smaller number. Fortunately, that number is not zero. There are some great math learning apps out there. You just have to choose wisely.

Wednesday, November 5, 2014

Against Answer Getting

"Correct answers are essential... but they're part of the process, they're not the product. The product is the math the kids walk away with in their heads." —Phil Daro
If you have not already watched Phil Daro's 17-minute video Against Answer Getting, you should do so right away. (I'll keep this post short to give you enough time to watch it in its entirety.)

Daro, a longtime mathematics educator and leading figure in the national mathematics education community, is currently director of the San Francisco field site of SERP, the Strategic Education Research Partnership. He was one of the mathematics educators who played a leading role in the formulation of the mathematics Common Core State Standards. (You know, one of those knowledgeable experts the StopCommonCore brigade keep claiming were not involved in CCSS development.)

The video is full of powerful insights that the mathematics education community has accumulated over many years of research. My opening quote sums up the focus of the video. Here is another one I like:
"Mathematics does not break down into lesson-sized pieces." Phil Daro
This particular quote resonates with me. I adopted the same principle in the design of my MOOC Introduction to Mathematical Thinking, currently about halfway through its fifth run.

Daro's focus, both in the video and in his work in general, is K-12 mathematics education. But it is very relevant to those of us in college-level mathematics education. When students come to college with a perception that mathematics is about "answer getting," we face the very uphill task of ridding them of that misleading mindset.

True, for hundreds of years, getting answers was a key component of learning and doing mathematics. But these days, if we want answers in mathematics, we generally use one of a number of digital technologies. The job of today's mathematician (or typical user of mathematics) is problem solving. The part that requires a human mind is when the problem has a novel aspect. It was precisely to put the focus on the thinking part that I named my MOOC the way I did.

The principle requirement for being able to solve a novel problem is conceptual understanding. That is why the issues Daro raises in that video are so central to the mathematics education of the citizens of tomorrow.

The outdated mindset about the purpose of mathematics that many students bring with them when they transition from school to college is not the only problem many have to overcome. A parallel issue manifests itself when they start to learn about mathematical proofs (if they follow the mathematics path).

My MOOC students are currently right in the middle of that part of the course (proofs), and many are having a very hard time coming to understand what role proofs play and what (therefore) constitutes a good proof.

The dominant perception is that proofs are what mathematicians produce in order to determine mathematical truth. That, of course, is true (at least in an idealistic sense that guides mathematical progress), but as with arithmetic answer getting, it is only part of the story. And in terms of actual mathematical practice, a very small part of the story.

As with answer getting in K-12 math, achieving a logically correct proof is a binary target (right or wrong), which make both very easy to evaluate for correctness and assign a numerical grade. (Ka-ching!)

But let's pause and ask ourselves how proofs work in practice. If you want to know if Fermat's Last Theorem is true, you consult a reliable source. Today, any moderately knowledgeable mathematician will tell you the answer: "Yes." Now you know.

But what if you want to know why it is true. That's when you need to look at a proof.

In terms of mathematical practice, proofs are about understanding. They are communicative devices we construct to convince ourselves and to convince others.

In my MOOC, because I cannot assume the students have access to individualized, expert feedback on their work, I do not ask them to construct proofs. But I do present them with a range of purported proofs, some correct, others not, and ask them to evaluate them. The evaluation is in terms both of logical correctness and communicative effectiveness.

To do this, I ask them to look at each purported proof in terms of five different factors: one logical correctness, the others focusing on communicative issues. Though the five factors are not independent variables, I ask them to treat them as such when evaluating a proof.

This is the part of the course where those students who have had some exposure to proofs in the K-12 system tend to do worse than those who are new to proofs. They are simply not able to approach a proof other than in the "answer getting" mode of "Is it logically correct?"

This shows up dramatically with extremal cases. When I present them with a carefully constructed argument that is logically correct but provides no explanation, they will give it high marks across the board. But faced with an argument that is superbly articulated but has a logical flaw, they are psychologically unable to evaluate the structure of the argument. "It's wrong," they keep saying. End of story (for them).

Of course, extremal examples are atypical, and often difficult to wrap our minds around. That's what makes them so valuable as learning devices. It's when the classroom rubber hits the road and we find ourselves using mathematical thinking in our lives or careers that it becomes important to have good communication skills.

Pick up a more advanced level mathematics book or research article and the chances are high that the arguments presented will contain errors. (Actually, the book does not have to be advanced. Euclid's Elements is littered with "proofs" that are not logically sound.) But if the arguments are well laid out, with adequate explanations, a suitably skilled reader can fix them as they go alongpossibly with help from someone else. (That's definitely the case with Elements, though it took two thousand years before David Hilbert noticed that Euclid's own arguments left a lot of work to be done to make them genuine "proofs.")

It's the same in software engineering. Any useful program will have bugs. But if the code is well structured, and adequately annotated, someone else can dive in and fix it whenever a flaw manifests. A good computer programmer is not someone who writes error-free, working code; it is someone who writes working code that can easily be fixed or modified.

I'll leave it as an exercise for the reader to identify the analogous issue in the natural sciences.

If those of us in the education business want to do the best we can to prepare our students for life in the 21st century, we need to recognize that in an era when technologies provide instant answers (facts), the one ability they will need above anything else is (creative, reflective) thinking.