Posts tagged ‘NCWIT’
Sarah Guthals, a CS Education Researcher, was identified by Forbes Magazine as one of the “30 under 30” scientists to watch in years to come. Congratulations to Sarah! She wrote an interesting blog post on imposter syndrome and the nomination.
I have suffered from imposter syndrome for at least a decade. I have worked hard, but it’s really hard for me to believe that I deserve what I have, or that the accomplishments that I’ve made are valid. I recognized my imposter syndrome when I was in my first year of grad school and since then I have been really trying to combat it — but I think instead I have just been ignoring it. Let’s see if I can explain it in the context of this weeks events.
When I found out I was nominated, I was very happy, but already feeling like a fraud. Am I really the one that should be nominated? What have I done to deserve it? I haven’t done anything alone (always had a team or partner).
Valerie Barr wrote a recent blog post about the state of the labor pool for STEM workers, especially in computing. I particularly liked her point about the need to provide learning opportunities to bring women back who have left the tech industry. Caroline Simard’s report on the needs of female mid-level tech managers (see blog post here) is what got me thinking about ebooks originally. Caroline’s female mid-level tech managers needed to learn about new technologies, while still balancing a demanding job and more family responsibilities than their male counterparts. That’s where I saw a need for something like our ebooks, to provide computing learning opportunities that fit into busy lives (see ebook post). I see Valerie calling for something similar — we need more pathways to learn about computing for adults (see blog post here), and those pathways might help us to broaden participation in computing.
It is true that the industry changes quickly in some ways, with new tools, new approaches, and new languages. But there is a rich pool of potential employees who are being completely overlooked. The many women who have left tech positions could be brought back in and given training to bring them up to speed on the newest languages and development practices. But this is a reasonable approach only if, at the same time, the tech industry makes a commitment to improving climate. There is no point in bringing back people who left tech if they are simply going to want to leave again in another 5 years. In fact, I imagine that bringing back a group of tech veterans who have greater maturity and experience could do wonders to improve climate in some of the tech companies. But the companies have to commit. And they have to recognize that you can still be a cutting edge agile company even if the average age of your employees ticks up a bit.
Lucy Sanders is one of my heroes, so I’m always happy to link to articles about her. The point she’s making below is particularly interesting, and relates to previous posts about “grit” (see link here), and to the “lean in” phenomenon.
NCWIT isn’t just about getting women into tech jobs. It’s about getting women to share their perspective and knowledge. It’s about making sure women are not avoiding those leadership jobs or shirking from innovation because of something called unconscious bias.”There’s a big conversation going on now with what we call ‘fixing women.’ You hear things like ‘If women were just more confident.’ Or ‘If women were only better risk takers.’ We don’t subscribe to that. And we don’t subscribe to men being the biased, evil ones because research shows that all of us have this bias about who does technology,” Sanders said. “The ultimate goal, of course, is to make sure women and men are innovating equally in technology.”
It’s an interesting and open question. Nathan Ensmenger suggests that we have no evidence that computer scientists need a lot of mathematics (math background has been correlated with success in CS classes, not in success in a CS career), but the emphasis on mathematics helped computing a male field (see discussion here). Mathematics has both been found to correlate with success in CS classes, and not correlate with success in object-oriented programming (excellent discussion of these pre-requisite skill studies in Michael Caspersen’s dissertation). It may be true that you don’t have to be good at mathematics to learn to code, but you may have to be good at mathematics to succeed in CS classes and to get along with others in a CS culture who assume a strong math background.
People who program video games probably need more math than the average web designer. But if you just want to code some stuff that appears on the Internet, you got all the math you’ll need when you completed the final level of Math Blaster. (Here’s a good overview of the math skills required for entry-level coding. The hardest thing appears to be the Pythagorean theorem.)
Barbara Ericson’s 2015 AP CS demographics analysis: Still No African-Americans Taking the AP CS Exam in 9 States
Normally, this is the time of the year when Barb writes her guest post about the AP CS exam-taker demographics. She did the analysis, and you can get the overview at this web page and the demographics details at this web page.
But before we got a chance to put together a blog post, Liana Heitin of EdWeek called her for an interview. They did a nice job summarizing the results (including interactive graphs) at the article linked below.
Some of the more interesting points (from Liana’s article):
No girls took the exam in Mississippi, Montana, or Wyoming. (Though Montana had no test-takers at all, male included, this year. Wyoming, which previously had no students take the test, had three boys take the exam in 2015).
Hawaii had the largest percentage of female test-takers, with 33 percent.
The overall female pass rate went up 3 percentage points, to 61 percent, from the year before.
Twenty-four girls took the test in Iowa, and 100 percent of them passed.”You don’t usually see 100 percent passing with numbers that big,” said Ericson. “Maybe five out of five pass. But 24 out of 24 is pretty cool.”
No African-American students took the exam in nine states: Idaho, Mississippi, Montana, New Hampshire, New Mexico, North Dakota, South Dakota, Utah, and Wyoming. That’s better than last year, though, when 13 states had no African-American test-takers.
Notably, Mississippi has the highest population of African-Americans—about half of the state’s high school graduates last year were black, according to the Western Interstate Commission for Higher Education. Yet of the five AP computer science test-takers, all were white or Asian and male.
Abstract: We share a vision of a society that is able to express problems and ideas computationally. Andrea diSessa called that computational literacy, and he invented the Boxer Programming Environment to explore the media of computational literacy. Education has the job of making citizens literate. Education systems around the world are exploring the question of what should all citizens know about computing and how do we provide that knowledge. The questions being asked are about public policy, but also about what does it mean to be expressive with computation and what should computing users know. The answers to these questions have implications for the future of human-centric computing.
I. Our Job: The first computer scientists set the goal to achieve a Computing-Literate Society.
II. Challenges to Achieving a Computing-Literate Society
Access and Diversity
Inverse Lake Wobegon Effect
Unanswered research questions of policymakers
III. Inventing New Kinds of Computing Education
Story #1: Contextualized Computing Education.
Story #2: Understanding the Needs of High School CS Teachers.
In July, Cynthia Lee, Leo Porter, Beth Simon, and I held a workshop (funded by the NSF IUSE program) for new faculty at research-intensive universities, to help them to be more effective and efficient teachers. We had eight new faculty attend. We taught them about peer instruction, worked examples, how to create a syllabus, techniques for dealing with plagiarism, how to make time for teaching, and how to create a more inclusive classroom. The response was terrific. As one participant told us, “I can’t believe how much actionable knowledge I picked up about teaching in just a day and a half!”
We’ll be inviting new faculty from research-intensive universities again in Summer 2016.
The below list was created by Cynthia Lee for the workshop participants. I loved it and asked if I could offer it here as a guest post. I’m grateful that she agreed.
- Email top performers on a recent homework or exam to congratulate them; be sure to include a diverse group.
- Personally invite a woman or minority student who is doing well to major in CS, apply to an internship, or go to grad school. If your TAs work with small groups of students in a discussion section, have them do this as well.1
- Review today’s lecture slides to make sure that your gender pronouns are varied, and not in ways that conform to stereotype.
- Avoid heteronormative examples (e.g., bijective function between sets “boys” and “girls”).
- When using arbitrary names in examples, choose a broader selection (Juan, Neha, Maria, Mohammed, instead of just Jane Doe and John Smith). To represent your school’s population, use a previous quarter roster for ideas.
- At the beginning of the quarter, ask each student to email you to introduce themselves by naming one of their core values, and one way that CS relates to or could be used in service of that core value (or write it down in class, and/or share with a neighbor in class).2
- Never say, “This UI is so easy your mom could use it” or “How would you explain this to your mom?” or other phrases that equate women with lack of tech savvy. 3
- Review today’s lecture slides to make sure that stock photos and illustrations with people in them include diverse races and genders in non-stereotyped roles.
- Believe that hard work and effective practice matters more than DNA. Your beliefs influence students’ beliefs and impact their performance. 4
- Take a moment in class today to encourage students to focus on their “slope,” not their “y-intercept.” That is, in the long run it matters how fast you’re growing and learning, not advantages or deficiencies in where you started. 5
- Start class today by telling the students you’re proud of them and how hard they are working. Tell them you are enjoying working with them this quarter.
- Start class today by renewing your encouragement to students to come to office hours. Explicitly instruct them how to do it: “you don’t need to have a particular question-you’re welcome to just stop by for 5 minutes to introduce yourself” and “I’m not just here for homework questions-if you are considering changing your major to CS and want to talk about it, if you want to know what it’s like to work as a software engineer, if you are thinking about applying to grad school but don’t know where to begin, I’m happy to discuss that kind of thing as well.”
- Have very clear written expectations for student work (coding style, project components, etc.). Where possible, show sample solutions exactly as you would want a student to write them (don’t just give a “sketch” of the solution).
- Allow and encourage pair programming on assignments. 6
- Provide students with clear and timely feedback, including class-wide distribution data. Women and minority students often fear the worst about their position relative to the class and can be reassured by data. 7
- After a midterm exam, step through the math showing that they can still pass the course even if they did poorly. It’s just some multiplication, but take the time to talk about it. Be factual-no need to “sugar coat”-but provide facts that will help students who think things are worse than they really are.
- When a student is speaking, wait for the student to finish then count “one one-thousand, two one-thousand” in your mind before responding. Both men and women are prone to prematurely cutting off women when they speak. You may do this unconsciously unless you consciously add that pause. 8
- Occasionally choose a lecture to actually write a tally of how many times you’ve called on men vs women in the class. Both men and women are prone to calling on men more often. You may do this unconsciously unless you consciously do otherwise. 9
- Actively mitigate when students may be intimidating each other. When a student uses jargon in a question (often one of those questions that is more of a boast than a real question), explicitly identify when you expect that most students will not be familiar with that jargon, and/or it is not something other students are expected to know for the class (“Thanks for your comment. For the rest of the class, I’m sure most of you aren’t familiar with some of those terms-don’t worry, you’re not alone. Those terms are outside the scope of this class and not necessary to know.”)
- Ensure that you and your TAs call each student by their preferred name and gender pronoun-including allowing students to write their preferred name on homework and exams-even if these do not match their current legal and/or registrar records of name and sex. This issue deeply affects transgender students, and also many students who prefer to have an alternate anglicized name. Some institutions are good about allowing students to easily make these changes with the registrar so the preference will automatically show up on your roster. Find out about your school’s policies. You could also put a statement in your syllabus that you welcome students to email you about their preference.
- Watch out for examples or anecdotes about your childhood or daily life that may cause students to feel excluded for economic reasons (e.g., talking about pricey gadgets or vacations in Hawaii as normal). Even if you know that you did not experience these things and are simply using them as an example, students don’t know that and can mistakenly assume you are referring to them in a normative way.
- Mid-quarter, reach out to a student who has filed a disability accommodation form with you and ask them if their needs are being met in your class. Reaffirm your commitment to complying with their approved accommodations and your willingness to receive complaints if there is a problem.
- Encourage your colleagues to do the items on this list. Advertise your good example by bringing up your performance of these items in conversations with other faculty.
- Holly Lord and Joanne McGrath Cohoon. “Recruiting and Retaining Women Graduate Students in Computer Science and Engineering,” 2006. ↩︎
- Research shows this intervention mitigates stereotype threat. Reduced racial gap by 30%. https://www.gsb.stanford.edu/insights/value-values-affirmation. ↩︎
- This sexist trope is something women have been working to expunge from our vocabulary. Unfortunately, still often seen in discussion of UI design. http://geekfeminism.wikia.com/wiki/So_simple,_your_mother_could_do_it ↩︎
- Carol Dweck. “The New Psychology of Success.” http://s3.amazonaws.com/ebsp/pdf/mindsett.pdf This research shows that minority students perform worse in classes where the professor believes in a “fixed mindset” (talent is innate) when compared to performance in classes where professor has a “growth mindset” (talent can be developed through effort). See also CS-specific work on mindsets: Laurie Murphy and Lynda Thomas. “Dangers of a fixed mindset: implications of self-theories research for computer science education.” ITiCSE 2008. ↩︎
- Articulating this idea as slope/y-intercept is from Professor John Ousterhout of Stanford. ↩︎
- Among other research showing benefits of pair programming: Leo Porter and Beth Simon. “Retaining nearly one-third more majors with a trio of instructional best practices in CS1,” SIGCSE ’13. http://dl.acm.org/citation.cfm?id=2445248 ↩︎
- These fears are related to “Imposter Syndrome”-even highly talented students from under-represented groups fear that they are unskilled, and more unskilled than everyone else. Overview of Imposter Syndrome research: https://en.wikipedia.org/wiki/Impostor_syndrome ↩︎
- Occasioned by a news item about a panel discussion in Silicon Valley, NYTimes reviews research on women being interrupted when speaking: http://nytlive.nytimes.com/womenintheworld/2015/03/19/google-chief-blasted-for-repeatedly-interrupting-female-government-official/ ↩︎
- Jere Brophy and Thomas Good. “Teachers’ communication of differential expectations for children’s classroom performance,” 1970. http://psycnet.apa.org/journals/edu/61/5/365.pdf ↩︎