Why ‘U.S. News’ should rank colleges and universities according to diversity: Essay from Dean Gary May #CSforAll
Georgia Tech’s Dean of Engineering Gary May was one of the advisors on “Georgia Computes!” He makes a terrific point in his essay linked below. Want broadened participation in computing (BPC)? CS for All? Make diversity count — and rankings are what “counts” in higher education today.
U.S. News & World Report, that heavyweight of the college rankings game, recently hosted a conference focused partially on diversity in higher education. I did an interview for the publication prior to the forum and spoke on a panel at the event.I was happy to do it. As dean of one of the country’s most diverse engineering schools, I am particularly invested in these issues. My panel focused on how to help women and underrepresented minority students succeed in STEM fields, and I’m grateful to U.S. News for leading the discussion.But the publication, for all its noble intentions, could do more to follow through where it counts. Diversity is currently given no weight in the magazine’s primary university and disciplinary rankings, and it’s time for that to change. As U.S. News goes, so goes higher education.
Malcolm Gladwell’s new podcast, Revisionist History, recently included a mini-series about the inequities in society that higher education perpetuates. Higher education is a necessity for a middle class life in today’s US, but not everyone gets access to higher education, which means that the economic divide grows larger. We in higher education (an according to Richard Tapia in his foreword to Stuck in the Shallow End, we in computer science explicitly) may be playing a role in widening the economic divide. David Brooks wrote about these inequities in 2005, in his NYTimes column, titled “The Education Gap“:
We once had a society stratified by bloodlines, in which the Protestant Establishment was in one class, immigrants were in another and African-Americans were in another. Now we live in a society stratified by education. In many ways this system is more fair, but as the information economy matures, we are learning it comes with its own brutal barriers to opportunity and ascent.
Gladwell has written about higher education before. In David and Goliath: Underdogs, misfits, and the art of battling giants, he told the story of Caroline Sacks who loved science since she was a little girl. When she applied to college, she was accepted into both University of Maryland and Brown University. She chose Brown for its greater prestige. Unfortunately, that prestige came with a much more competitive peer set. Caroline compared herself to them, and found herself wanting. She dropped out of science. Gladwell suggests that, if she’d gone to Maryland, she might have persisted in science because she would have fared better in the relative comparison.
Gladwell’s three podcasts address who gets in to higher education, how we pay for financial aid for poorer students, and how we support institutions that serve poorer students.
In Carlos doesn’t remember, Gladwell considers whether there are poorer students who have the academic ability to succeed but aren’t applying to colleges. Ivy League schools are willing to offer an all-expenses-paid scholarship to qualified students whose family income is below a certain level, but they award few of those scholarships. The claim is that there are just few of those smart-enough-but-poor students. Economists Avery and Hoxby explored that question and found that there are more than 35,000 students in the United States who meet the Ivy League criteria (see paper here). So why aren’t they applying for those prestigious scholarships?
Gladwell presents a case study of Carlos, a bright student who gets picked up by a program aimed at helping students like him get access to high-quality academic opportunities. Gladwell highlights the range of issues that keep students like Carlos from finding, getting into, and attending higher education opportunities. He provides evidence that Avery and Hoxby dramatically underestimate the high-achieving poor student, e.g., Avery and Hoxby identified some students using eighth grade exam scores. Many of the high-achieving poor students drop out before eighth grade.
As an education researcher, I’m recommending this podcast to my graduate students. The podcast exemplifies why it’s so difficult to do interview-based research. The title of the episode comes from Carlos’s frequent memory lapses in the interview. When asked why he didn’t mention the time he and his sister were taken away from their mother and placed in foster care, Carlos says that he doesn’t remember that well. It’s hard to believe that a student this smart forgets something so momentous in his life. Part of this is a resilience strategy — Carlos has to get past the bad times in his life to persist. But part of it is a power relationship. Carlos is a smart, poor kid, and Gladwell is an author of international bestsellers. Carlos realizes that it’s in his best interest to make Gladwell happy with him, so he says what he thinks Gladwell wants to hear. Whenever there is a perceived power gap between an interviewee (like Carlos) and an interviewer (Gladwell), we should expect to hear not-quite-the-truth. The interviewee will try to tell the interviewer what he thinks the world-famous author wants to hear — not necessarily what the interviewee actually thinks.
The episode Food Fight contrasts Bowdoin College in Maine and Vassar College in New York. They are similar schools in terms of size and academics, but Bowdoin serves much better food in its cafeterias than Vassar. Vassar made an explicit decision to cut back in its food budget in order to afford more financial aid to its poorer students. Vassar spends almost twice as much as Bowdoin in financial aid, and has a much higher percentage of low-income students than Bowdoin. Vassar is explicit in the trade-offs that they’re making. Gladwell interviews a student who complains about the food quality, but says that she accepts it as the price for having a more diverse student body.
But there’s a tension here. Vassar can only afford that level of financial aid because there is a significant percentage of affluent students who are playing full fare — and those affluent students are exactly the ones for which both Bowdoin and Vassar compete. Vassar can’t balance their budget without those affluent students. They can’t keep providing for the poorer students unless they keep getting their share of the richer students. Here’s where Gladwell starts the theme he continues into the third episode, when he tells his audience, “Never give to Bowdoin!”
The third episode, My Little Hundred Million, starts from Hank Rowan giving $100 million to Glassboro State University in New Jersey. At the time, it was the largest philanthropic gift ever to a higher education institution. Since then there have been others, but all to elite schools. Rowan’s gift made a difference, saving a nearly-bankrupt university that serves students who would never be accepted at the elites. It made a difference in providing access and closing the “Education Gap,” in exactly the way that David Brooks was talking about in 2005. So why are such large gifts going instead to schools like Stanford and Harvard, who don’t play a role in closing that gap? And why do the rich keep giving to the elite institutions? Gladwell continues the refrain from the last episode. Stop giving to Harvard! Stop giving to Stanford!
The most amazing part of the third episode is an interview with Stanford President, John Hennessy. Gladwell prods him to defend why Stanford should get such large gifts. Hennessy talks about the inability of smaller, less elite schools to use the money well. Do they know how to do truly important things with these gifts? It’s as if Hennessy doesn’t understand that simply providing access to poor students is important and not happening. Hennessy is painted by Gladwell as blind to the inequities in the economy and to who gets access to higher education.
I highly recommend all of Revisionist History. In particular, I recommend this three-part mini-series for readers who care about the role that higher education can play in making our world better. Gladwell tells us that higher education has a critical role to play, in terms of accepting a more diverse range of students through our doors. We won’t do much to address the problems by only focusing on the “best and brightest.” As Richard Tapia writes in his foreword to Stuck in the Shallow End, that phrase describes much of what we get wrong in higher education.
“Over the years, I have developed an extreme dislike for the expression ‘the best and the brightest,’ so the authors’ discussion of it in the concluding chapter particularly resonated with me. I have seen extremely talented and creative underrepresented minority undergraduate students aggressively excluded from this distinction. While serving on a National Science review panel years back, I learned that to be included in this category you had to have been doing science by the age of ten. Of course, because of lack of opportunities, few underrepresented minorities qualified.”
Closing the Education Gap requires us to think differently about who we accept into higher education, who we most need to be teaching, and how we pay for it.
White House Call to Action: Incorporating Active STEM Learning Strategies into K-12 and Higher Education
I’m so happy to see this! I’ve received significant pushback on adopting active learning among CS faculty. Maybe a White House call can convince CS higher education faculty to adopt active learning strategies?
Active learning strategies include experiences such as:
- Authentic scientific research or engineering or software design in the classroom to help students understand the practice of science, technology, and engineering and promote deep learning of the subject matter;
- Interactive computer activities to support students’ exposure to trial-and-error and promote deep learning;Discussions to encourage collaboration and idea exchange among students; and
- Writing to generate original ideas and solidify knowledge.
Today, the White House Office of Science and Technology Policy is issuing a call to action to educators in K-12 and higher education, professional development providers, non-profit organizations, Federal agencies, private industry, and members of the public to participate in a nationwide effort to meet the goals of STEM for All through the use of active learning at all grade levels and in higher education.
Women 1.5 Times More Likely to Leave STEM Pipeline after Calculus Compared to Men: Lack of Mathematical Confidence a Potential Culprit
When you read this paper, consider Nathan Ensmenger’s assertion that (a) mathematics has been show to predict success in CS classes but not in computing careers and (b) increasing mathematics requirements in undergraduate CS may have been a factor in the decline in female participation in computing.
Our analyses show that, while controlling for academic preparedness, career intentions, and instruction, the odds of a woman being dissuaded from continuing in calculus is 1.5 times greater than that for a man. Furthermore, women report they do not understand the course material well enough to continue significantly more often than men. When comparing women and men with above-average mathematical abilities and preparedness, we find women start and end the term with significantly lower mathematical confidence than men. This suggests a lack of mathematical confidence, rather than a lack of mathematically ability, may be responsible for the high departure rate of women. While it would be ideal to increase interest and participation of women in STEM at all stages of their careers, our findings indicate that if women persisted in STEM at the same rate as men starting in Calculus I, the number of women entering the STEM workforce would increase by 75%.
When I give talks about teaching computer to everyone, I often start with Alan Perlis and C.P. Snow in 1961. They made the first two public arguments for teaching computer science to everyone in higher education. Alan Perlis’s talk was the most up-beat, talking about all the great things we can think about and do with computer. He offered the carrot. C.P. Snow offered the stick.
C.P. Snow foresaw that algorithms were going to run our world, and people would be creating those algorithms without oversight by the people whose lives would be controlled by them. Those who don’t understand algorithms don’t know how to challenge them, to ask about them, to fight back against them. Quoting from Martin Greenberger’s edited volume, Computers and the World of the Future (MIT Press, 1962), we hear from Snow:
Decisions which are going to affect a great deal of our lives, indeed whether we live at all, will have to be taken or actually are being taken by extremely small numbers of people, who are nominally scientists. The execution of these decisions has to be entrusted to people who do not quite understand what the depth of the argument is. That is one of the consequences of the lapse or gulf in communication between scientists and non-scientists. There it is. A handful of people, having no relation to the will of society, have no communication with the rest of society, will be taking decisions in secret which are going to affect our lives in the deepest sense.
I was reminded of Snow’s quote when I read the article linked below in the NYTimes. Increasingly, AI algorithms are controlling our lives, and they are programmed by data. If all those data are white and male, the algorithms are going to treat everyone else as outliers. And it’s all “decisions in secret.”
This is fundamentally a data problem. Algorithms learn by being fed certain images, often chosen by engineers, and the system builds a model of the world based on those images. If a system is trained on photos of people who are overwhelmingly white, it will have a harder time recognizing nonwhite faces.
A very serious example was revealed in an investigation published last month by ProPublica. It found that widely used software that assessed the risk of recidivism in criminals was twice as likely to mistakenly flag black defendants as being at a higher risk of committing future crimes. It was also twice as likely to incorrectly flag white defendants as low risk.
The reason those predictions are so skewed is still unknown, because the company responsible for these algorithms keeps its formulas secret — it’s proprietary information. Judges do rely on machine-driven risk assessments in different ways — some may even discount them entirely — but there is little they can do to understand the logic behind them.
One of our superstar alumna, Joy Buolamwini, wrote about a similar set of experiences. She’s an African-American woman who works with computer vision, and the standard face-recognition libraries don’t recognize her. She lays the responsibility for fixing these problems on the backs of “those who have the power to code systems.” C.P. Snow would go further — he’d say that it’s all our responsibility, as part of a democratic process. Knowing about algorithms and demanding transparency when they effect people’s lives is one of the responsibilities of citizens in the modern world.
The faces that are chosen for the training set impact what the code recognizes as a face. A lack of diversity in the training set leads to an inability to easily characterize faces that do not fit the normal face derived from the training set.
So what? As a result when I work on projects like the Aspire Mirror (pictured above), I am reminded that the training sets were not tuned for faces like mine. To test out the code I created for the Aspire Mirror and subsequent projects, I wore a white mask so that my face can be detected in a variety of lighting conditions.
The mirror experience brings back memories from 2009. While I was working on my robotics project as an undergraduate, I “borrowed” my roommate’s face so that I could test the code I was writing. I assumed someone would fix the problem, so I completed my research assignment and moved on.
Several years later in 2011, I was in Hong Kong taking a tour of a start-up. I was introduced to a social robot. The robot worked well with everyone on the tour except for me. My face could not be recognized. I asked the creators which libraries they used and soon discovered that they used the code libraries I had used as an undergraduate. I assumed someone would fix the problem, so I completed the tour and moved on.
Seven years since my first encounter with this problem, I realize that I cannot simply move on as the problems with inclusion persist. While I cannot fix coded bias in every system by myself, I can raise awareness, create pathways for more diverse training sets, and challenge us to examine the Coded Gaze — the embedded views that are propagated by those who have the power to code systems.
My Blog@CACM post for this month is on JES, the Jython Environment for Students, which at 14 years old and over 10,000 downloads, is probably one of the oldest, most used, and (by some definition) most successful pedagogical Python IDE’s.
The SIGCSE Members list recently had a discussion about moving from Python 2 to Python 3. Here’s a description of differences. Some writers asked about MediaComp. With respect to the Media Computation libraries, one wrote:
I’m sad about this one, because we use and like this textbook, but I think it’s time to move to Python 3. Is there a compatible library providing the API used in the text?
Short answer: No. There are no compatible Media Computation libraries for CPython 2 or 3.
We keep trying. The latest attempt to build Media Computation libraries in CPython is here: https://github.com/sportsracer48/mediapy. It doesn’t work on all platforms yet, e.g., I can’t get it to load on MacOS.
We have yet to find a set of libraries in Python that work cross-platform identically for sample-level manipulations of sounds. For example, PyGame’s mixer object doesn’t work exactly the same on all platforms (e.g., sampling rates aren’t handled the same on all platforms, so the same code plays different speed output on different platforms). I can do pixel-level manipulations using PIL. We have not yet tried to find libraries from frame manipulations of video (as individual images). I have just downloaded the relevant libraries for Python 3 and plan to explore in the future, but since we can’t make it work yet in Python 2 (which has more mature libraries), I doubt it will work in Python 3.
I complained about this problem in my blog in 2011 (see post here). The situation is better in other languages, but not yet in Python.
- I have been building Media Computation examples in GP, a blocks-based language (see post here).
- Jeff Gray’s group at U. Alabama has built Blockly-like languages Pixly and Tunely for pixel and sample level manipulations.
- Cynthia Lee at Stanford has been doing Media Computation in her classes in MATLAB and in C++
- The Calico project supports Media Computation in IronPython (based on Python 3) and many other languages, because it builds on .NET/MONO which has good multimedia support.
When we did the 4th edition of our Python Media Computation textbook, I looked into what we’d have to change in the book to move to Python 3. There really wasn’t much. We would have to introduce
Yasmin Kafai has been a friend and mentor to me for years — she introduced me to my PhD advisor, Elliot Soloway. Her book with Quinn Burke, Connected Code, updates thinking about the role of computing and programming in schools. They emphasize an idea they call Computational Participation as a contrast with computational thinking. I asked Yasmin to do a CACM Viewpoint on the idea, and it’s published this month. Yasmin has shared the paper on Academia.edu.
In the 1980s many schools featured Basic, Logo, or Pascal programming computer labs. Students typically received weekly introductory programming instruction. These exercises were often of limited complexity, disconnected from classroom work, and lacking in relevance. They did not deliver on promises. By the mid-1990s most schools had turned away from programming. Pre-assembled multimedia packages burned onto glossy CD-ROMs took over. Toiling over syntax typos and debugging problems were no longer classroom activities.
Computer science is making a comeback in schools. We should not repeat earlier mistakes, but leverage what we have learned. Why are students interested in programming? Under what circumstances do they do it, and how? Computational thinking and programming are social, creative practices. They offer a context for making applications of significance for others, communities in which design sharing and collaboration with others are paramount. Computational thinking should be reframed as computational participation.