This is a guest post by P.J. Karafiol. P.J. has been in high school education for 20 years, the last fifteen in Chicago Public Schools as a math teacher and department chair, curriculum coordinator, and, this year, Assistant Principal/Head of High School. P.J. is the head author for the ARML competition, the founder of Math Circles of Chicago, and until last year a dedicated math team coach. He lives with his wife, three children, and two dogs in Chicago, about a half-mile from the public high school he attended.
I loved your post asking how we can make math enrichment less elitist, and I wanted to let you (and your readers) know about what we’re doing about that here in Chicago. In 2010, inspired in part by your post about why math contests kind of suck, my department (at Walter Payton College Prep) and I decided to start a math circle in Chicago. We rounded up some of our friends from the city and suburbs, used part of an award from the Intel Foundation as seed money, and launched the Payton Citywide Math Circle. We had three major tenets: that students should be solving challenging problems, not listening to lectures; that the courses should be open to anyone who wanted to join; and that the program should be 100% free.
We’ve grown tremendously in the five years since our first Saturday afternoon session. Renamed Math Circles of Chicago, we now run math enrichment programs after school and on Saturdays at five locations around the city, including some of the city’s poorest neighborhoods. In 2011 we partnered with the University of Illinois at Chicago, and we’ve been excited to welcome professors from all five of Chicago’s major universities as teachers and partners. We currently serve 500 students in grades 5-12 (the vast majority in grades 5-8), and we provide them with opportunities they don’t get anywhere else: above all, the opportunity to do challenging mathematics in an environment of collaborative exploration. And, since 2013, we’ve sponsored Chicago’s only (and, to my knowledge, the nation’s second) youth math research symposium, QED. This year, over 150 students in grades 5-12 brought original math research projects to the symposium, and we’ve trained teachers across the city in how to support math research in their own classes.
You’re absolutely right that we need to make opportunities like this available to all students. When I was in fifth grade, I told my father I “hated math”. He responded by taking me to a local (university) bookstore to let me peruse the wall of yellow Springer books. After I opened and closed my third incomprehensible tome, he explained that mathematics was what was in those books; the subject I hated in school was arithmetic. But I was an only child; many of our students tell us that what they do at math circle–graph theory, number theory, geometry explorations, etc.–is nothing like what is taught in their math classes. Frankly, I wouldn’t call what we do math “enrichment” at all: for many of the kids we serve, math circle is the only exposure they get to what I (or my father) would call real mathematics.
Researchers such as Mary Kay Stein would agree with our assessment. Stein divides math tasks into four levels of complexity, from “memorization” (level 1) to following procedures (levels 2 and 3, depending on whether the procedures are connected to genuine mathematical content). She reserves the term “doing mathematics” for the highest level of her framework, when students are solving problems for which they haven’t yet learned a procedure. (You can find a summary of her framework here.) One area where we’re growing is that we’re trying to engage even more teachers from Chicago Public Schools–not just our founders–in teaching problem-based sessions, in the expectation that those experiences will change what they do in the classroom for their “regular” students, as those experiences did for us.
Although we’ve grown and evolved, we’ve never strayed from our initial tenets. The Intel money ran out long ago, but we’re entirely donor-supported; our families donate the majority of our annual operating costs on an entirely voluntary basis. (We call it the “NPR Model”: if you like what you hear and think it’s valuable, please contribute what you can.) Students still come from all over the city and still spend their time solving and discussing mathematics, generating questions as well as answering them–not listening to lectures or doing practice worksheets. And our only admission requirement is the same as it was in 2010: students have to write, by hand (no typing allowed!), a one-page essay about why they want to do math on Saturdays (or after school). We have a waiting list in the dozens for each of our three largest sites.
If your readers want to learn more, or to help out, I’d encourage them to visit our website at mathcirclesofchicago.org, or to email our executive director, Doug O’Roark, at doug (at) mathcirclesofchicago (dot) org. We can always use donations; the program costs us about $20 per student per Saturday, and many of our families can’t afford to give nearly that much. We also support other noncompetitive math opportunities for our students: in addition to telling them about programs like the University of Chicago’s Young Scholars Program (now as of 2015 our official partner), HCSSiM, MITES, and PROMYS, we subsidize travel and other expenses for students whose financial aid awards are insufficient.
We’re really excited about the work we’re doing in Chicago. We’ve shown that math circles can exist (and thrive) outside of traditional university environments, and that placing circles in schools and community centers–and partnering with local community organizations–brings more students, and a more diverse group of students. Our programs are currently growing faster than our fundraising–which is a great problem to have–so we really could use any support your readers want to give. We’d also welcome visitors; we’re excited to help people see real kids do real math.
After I left the bookstore that afternoon 34 years ago, I did come to love math–a love supported not just by math contests, but by wonderful opportunities to learn and do mathematics at Dr. Ross’s program at OSU and at HCSSiM, where you and I met in 1987. Without those programs, I would be a different person today. So thank you for drawing attention to this critical issue.
Founder and President
Math Circles of Chicago
I’m sure many of you have heard the story that a tenured professor, as well as a non-tenured professor, were fired recently by the president, Simon Newman, of Mount St. Mary’s school in Maryland.
The short version: Newman, a private equity asshole, got confused as to where he was working and decided to fire anyone who disagreed with him, referring to disloyalty as the cause.
The specific “act of disloyalty” one of the professors made was to allow a student newspaper to report a (true) comment the president didn’t want made public, namely:
“This is hard for you because you think of the students as cuddly bunnies, but you can’t,” Mr. Newman is quoted as saying. “You just have to drown the bunnies.” He added, “Put a Glock to their heads.”
OK, gross and shocking.
But personally, I was even more disgusted by the story behind this story, namely his underlying plan to get rid of students for the sake of improving the college’s “retention rate” and thus its ranking on the US News & World Reports College rankings, that scourge of higher education.
The original article from the student newspaper explains Newman’s unfuckingbelievable plan. From the article:
Mount St. Mary’s University, like all colleges and universities in the U.S., is required by the federal government to submit the number of students enrolled each semester. The Mount’s cutoff date for the Fall 2015 semester was Sept. 25, and the number of students enrolled as of that date would be the number used to compute the Mount’s student retention.
Newman was obsessed with getting rid of students and revealed this in an email:
Newman’s email continued: “My short term goal is to have 20-25 people leave by the 25th [of Sep.]. This one thing will boost our retention 4-5%. A larger committee or group needs to work on the details but I think you get the objective.”
How was he going to achieve this number?
The president’s plan to “cull the class” involved using a student survey that was developed in the president’s office and administered during freshman orientation.
The survey was going to be given to students and started out by describing itself as “based on some of the leading thinking in the area of personal motivation and key factors that determine motivation, success, and happiness. We will ask you some questions about yourself that we would like you to answer as honestly as possible. There are no wrong answers.”
The actual plan for the results of the survey were a bit different – they would be used to help compile a list of students to get rid of before the deadline. Just so gross, and a wonderful example of how an algorithm can be used for good or evil. Please read the rest of the article, it’s amazing journalism.
Holy crap, people, this gaming of the US News & World Reports model has got to stop, this shit is nuts. And it makes me wonder how many other places are doing stuff like this and not getting caught. I mean, at least at this university the president was stupid enough to tell the professors the plan, right?
There’s a great article in the Atlantic that’s making waves on my Facebook page (granted, my Facebook feed has more than its share of math nerds).
Called The Math Revolution and written by Peg Tyre, the piece describes the recent proliferation of math education programs for young people, which include the old-fashioned things I grew up with like math team and HCSSiM, but also include new stuff I’ve heard about (Russian math circles, Art of Problem Solving) as well as stuff I’ve never heard of (MathPath, AwesomeMath, MathILy, Idea Math, sparc, Math Zoom, and Epsilon Camp).
What I like about this piece is it directly addresses something that has bothered me for years and has, frankly, kept me from devoting myself to creating or running one of these programs. Namely, the extreme elitism involved. From the article:
And since many of the programs are private, they are well out of reach for the poor. (A semester in a math circle can cost about $300, a year at a Russian School up to $3,000, and four weeks in a residential math program perhaps twice that.) National achievement data reflect this access gap in math instruction all too clearly. The ratio of rich math whizzes to poor ones is 3 to 1 in South Korea and 3.7 to 1 in Canada, to take two representative developed countries. In the U.S., it is 8 to 1. And while the proportion of American students scoring at advanced levels in math is rising, those gains are almost entirely limited to the children of the highly educated, and largely exclude the children of the poor. By the end of high school, the percentage of low-income advanced-math learners rounds to zero.
So my question today, dear readers, is how to address this problem, which I assume starts before kindergarten. Do we just expand math enrichment programs so much that they eventually become accessible to more people?
And beyond access, how could we possibly keep costs down, considering that the people who are competent to teach this stuff have other lucrative offers?
It’s clearly a transitioning problem to some extent, since once we have enough people who speak “fun math,” there will be enough people to train the next generation. And the beauty of math is that you really only need a stick in the sand (and time, and a devoted teacher and ready students) to make it happen.
Hey if you haven’t read it yet take a look at this new blog, called Time for One More.
Gorgeous writing, and an inspiration for my new project on thinking about the elderly and technology.
One thing I call for in the essay is the teaching of ethics to aspiring data scientists, and yesterday some very cool professors from the Berkeley School of Information wrote to me and told me about their two classes on data science and ethics, one for undergrads and the other for graduate students. I seriously wish I could enroll in them!
Please tell me of other efforts in this direction if you know of them.
People who make their living writing and deploying algorithms like to boast that they are fair and objective simply because they are algorithmic and mathematical. That’s bullshit, of course.
For example, there’s this recent Washington Post story about an algorithm trained to detect “resting bitch face,” or RBF, which contains the following line (hat tip Simon Rose):
FaceReader, being a piece of software and therefore immune to gender bias, proved to be the great equalizer: It detected RBF in male and female faces in equal measure. Which means that the idea of RBF as a predominantly female phenomenon has little to do with facial physiology and more to do with social norms.
While I agree that social norms have created the questions RBF phenomenon, no algorithm is going to prove that without further inquiry. For that matter, I don’t even understand how the algorithm can claim to understand neutrality of faces at all; what is their ground truth if some people look non-neutral when they are, by definition, neutral? The answer entirely depends on how the modeler creates the model, and those choices could easily contain gender bias.
So, algorithms are not by their nature fair. But sometimes their specific brand of unfairness might still be an improvement, because it’s at least measurable. Let me explain.
Take, for example, this recent Bloomberg piece on the wildly random nature of bankruptcy courts (hat tip Tom Adams). The story centers on Heritage, a Texas LLC, which bought up defaulted mortgages and sued 210 homeowners in court, winning about half. Basically that was their business plan, a bet that they’d be able to get lucky with some judges and the litigation courts because they knew how to work the system, even though in at least one case it was decided they didn’t even have standing. Here’s the breakdown:
Now imagine that this entire process was embedded in an algorithm. I’m not saying it would be automatically fair, but it would be much more auditable than what we currently have. It would be a black box that we could play with. We could push through a case and see what happens, and if we did that we might create a system that made more sense, or at least became more consistent. If we found that one case didn’t have standing, we might be able to dismiss all similar cases.
I’m not claiming we want everything to become an algorithm; we already have algorithmized too many things too quickly, and it’s brought us into a world where “big data blacklisting” is a thing (one big reason: the current generation of algorithms often work for people in power).
Algorithms represent decision processes that are vulnerable to inspection more than most human-led processes are. And although we are not taking advantage of this yet, we could and should do so soon. We need to start auditing our algorithms, at least the ones that are widespread and high impact.
I’m excited to be traveling to Harvard next Monday to give a talk at the Data Privacy Lab. The projects going on at the Data Privacy Lab are privacy-related: re-identification, discrimination in online ads, privacy-enhanced linking, fingerprint capture, genomic privacy, and complex-care patients.
My talk will not be entirely focused on privacy – it will basically be a somewhat technical version of my book followed by my proposals for technological tools that could address the problems associated with opaque, widespread, and destructive algorithms (my definition of a “Weapon of Math Destruction”. Specifically, I want to examine the question of how we understand a black-box algorithm in terms of measuring its outputs (as opposed to scrutinizing the source code).
The Data Privacy Lab is run by Latanya Sweeney, a hero of mine who did great work in detecting online discrimination in Google ads among other things. I’m hoping to meet her first because it’s always nice to meet your hero but also because, as the chief technologist at the Federal Trade Commission, she can give me perspective on the kind of technological tools that regulators such as the FTC and the CFPB might actually adopt (or develop).
In other words, I don’t want to spend 4 years developing tools that nobody would use. On the other hand, I have the impression that they generally speaking don’t know what kind of tools are possible.