Posts tagged ‘NCWIT’
Steps Teachers Can Take to Keep Girls and Minorities in Computer Science Education | Cynthia Lee in KQED News
So glad to see Cynthia Lee’s list (described in this blog post) get wider coverage.
Last summer, Cynthia Lee, a lecturer in the computer science department at Stanford University, created a widely-circulated document called, “What can I do today to create a more inclusive community in CS?” The list was developed during a summer workshop funded by the National Science Foundation for newly hired computer science faculty and was designed for busy educators. “I know the research behind these best practices,” said Lee, “but my passion comes from what I’ve experienced in tech spaces, and what students have told me about their experiences in computer science classrooms.”
Too often students from diverse backgrounds “feel that they simply aren’t wanted,” said Lee. “What I hear from students is that when they are working on their assignments, they love [computer science]. But when they look up and look around the classroom, they see that ‘there aren’t many people like me here.’ If anything is said or done to accentuate that, it can raise these doubts in their mind that cause them to questions their positive feelings about the subject matter.”
Google’s latest reports from their collaboration with Gallup lines up with Miranda Parker’s research interests in privilege in CS education (see preview of her RESPECT 2015 paper here). I invited her to write a guest blog post introducing the new reports. I’m grateful that she agreed.
Google, in collaboration with Gallup, has recently released new research about racial and gender gaps in computer science K-12 classrooms. A lot of the report confirms what we already knew: there are structural and social barriers that limit access to CS for black, Hispanic, and female students. I don’t mind the repeated results though–it helps form an even stronger argument that there is a dearth of diversity in computing classrooms across the country.
The report does highlight interesting tidbits that may not have been as obvious before. For example, black and Hispanic students are 1.5 and 1.7 times more likely than white students to be “very interested” in learning computer science. This knowledge, combined with the data that black and Hispanic students are less likely to have access to learning CS, creates a compelling argument for growing programs focused at these groups.
Research like this continues to push the envelope of what is known about racial and gender gaps in computer science. However, it may be time to dig deeper than visible identities and explore if there are other variables that, independently or together with the other traits, create a stronger argument for why the diversity gap exists. Does socioeconomic status better explain racial gaps? What about spatial ability? These are variables that we at Georgia Tech are looking at, as we hypothesize about what can be done to level the playing field in computing.
Today, we’re releasing new research from our partnership with Gallup that investigates the demographic inequities in K-12 computer science (CS) education in two reports, Diversity Gaps in Computer Science: Exploring the Underrepresentation of Girls, Blacks and Hispanics and Trends in the State of Computer Science in U.S. K-12 Schools. We surveyed 16,000 nationally representative groups of students, parents, teachers, principals, and superintendents in the U.S. Our findings explore the CS learning gap between white students and their Black and Hispanic peers as well as between boys and girls and confirm just how much demographic differences matter. We’re excited to share this data to bring awareness to issues on the ground in order to help expand CS education in meaningful ways.
Yep. Though I’ve seen a lot of in-classroom culture that drives out women, the bigger driver is that computing culture drives out many people, like the Stack Overflow results recently mentioned here.
Engineering classes and assignments do not “weed out” women; indeed, data show that women students do as well or better than male students in their course work. Instead, women students often point to the culture of engineering itself as a reason for leaving engineering.
This starts with activities that are designed to show novices how the profession actually does its work, how to interact with clients and other professionals, and how to exercise discretionary judgment in situations of uncertainty. Many discover that the engineering profession is not as open to being socially responsible as they hoped.
And, during the more informal, out-of-classroom training and socialization, women experience conventional gender discrimination that leaves them marginalized. These factors appear to be the main reasons these accomplished women leave their chosen profession.
Stack Overflow is an often used resource by programmers today. It’s also a barrier to women entering computing. Here’s a blog post summarizing a recent study on why women find Stack Overflow so unwelcoming.
There are many movements to get women into programming, but what about keeping them there? If they don’t feel comfortable using the resources that are available for all programmers then that is a big problem for retention in the field. To do our part in being more proactive in welcoming women into the field, we sought to uncover some reasons for this low participation.
When I visited Mumbai for LaTICE 2016, I mentioned meeting Yogendra Pal. I was asked to be a reader for his thesis, which I found fascinating. I’m pleased to report that he has now graduated and his thesis, A Framework for Scaffolding to Teach Vernacular Medium Learners, is available here: https://www.cse.iitb.ac.in/~sri/students/#yogendra.
I learned a lot from Yogendra’s thesis, like what “vernacular medium learners” means. Here’s the problem that he’s facing (and that Yogendra faced as a student). Students go through primary and secondary school learning in one language (Hindi, in Yogendra’s personal case and in his thesis), and then come to University to study Computer Science. Do you teach them (what Yogendra calls “Medium of Instruction” or MoI) in English, or in Hindi? Note that English is pervasive in Computer Science, e.g., almost all our programming languages use English keywords.
Here’s Yogendra’s bottomline finding: “We find that self-paced video-based environment is more suitable for vernacular medium students than a classroom environment if English-only MoI are used.” Yogendra uses a design-based research methodology. He measures the students, tries something based on his current hypothesis, then measures them again. He compares what he thought would happen to what he saw, and revises his hypothesis — and then iterate. Some of the scaffolds he tested may seem obvious (like using a slower pace), but a strength of the thesis is that he develops rationale for each of his changes and tests them. Eventually, he came to this surprising (to me) and interesting result: It’s better to teach with Hindi in the classroom, and in English when students are learning from self-paced videos.
The stories at the beginning of the thesis are insightful and moving. I hadn’t realized what a handicap it is to be learning English in a class that uses English. It’s obvious that the learners would be struggling with the language. What I hadn’t realized was how hard it is to raise your hand and ask questions. Maybe you have a question just because you don’t know the language. Maybe you’ll expose yourself to ridicule because you’ll post the question wrong.
Yogendra describes solutions that the Hindi-speaking students tried, and where the solutions didn’t work. The Hindi-speaking students used English-to-English dictionaries. They didn’t want English-Hindi dictionaries, because they wanted to become fluent in English, but they needed help with the complicated (especially technical) words. They tried using online videos for additional explanations of concepts, but most of those were made by American or British speakers. When you’re still learning English, switching from an Indian accent to another accent is a barrier to understanding.
The middle chapters are a detailed description of Yogendra’s attempts to scaffold student learning. He tried to teach in all-Hindi but some English technical terms like “execute” don’t have a direct translation in Hindi. He selected other Hindi words to represent the technical terms, but the words he selected as the Hindi translation were unusual and not well-known to the students. Perhaps the most compelling insight for me in these chapters was how important it was to both the students and the teachers that the students learn English — even when the Hindi materials were measurably better for learning in some conditions.
In the end, he found that Hindi language screencasts led to better learning (statistically significantly) when the learners (who had received primary and secondary school instruction in Hindi) were in a classroom, but that the English language screencasts led to better learning (again, statistically significantly) when the learners were watching the screencasts self-paced. When the students are self-paced, they can rewind and re-watch things that are confusing, so it’s okay to struggle with the English. In the classroom, the lecture just goes on by. It works best if it’s in Hindi for the students who learned in Hindi in school.
Yogendra tells a convincing story. It’s an interesting question of how these lessons transfer to other contexts. For example, what are the issues for Spanish-speaking students learning CS in the United States? In a general form, can we use the lessons from this thesis to make CS learning accessible to more ESL (English as a Second Language) learners?
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.