Posts tagged ‘NCWIT’
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 ↩︎
I recently moved offices. In the process of packing and pitching, I found the above editorial from the Georgia Tech student newspaper. Dated September 2002, it urged the faculty in the Liberal Arts, Architecture, and Management Colleges to reject the newfangled Media Computation class that was being proposed.
I had heard the argument being made in the editorial before, and continue to hear it today. The argument is that we do our students a disservice if we don’t give them “real” computer science. The editor cited above is arguing that all students at Georgia Tech deserve the same high-quality computer science education. If we don’t give them the “real” thing, if liberal arts and management majors aren’t getting the same thing as CS majors, they are only getting “CS lite.”
That phrase “CS lite” gets applied to our BS in Computational Media regularly. (See the blog post where I talk about that.) Which is funny, because all but one of the CS classes that CM majors take are the same ones that CS majors take. Georgia Tech CS majors take many more credit hours than other majors (including CS majors at other institutions), and the CM major has enough CS courses to be ABET accredited as a computing program. So, what’s “lite” about that? Are other schools’ BS in CS programs “Georgia Tech CS lite” because they have fewer credit hours in CS?
Media Computation wasn’t lite. It was different. MediaComp didn’t cover everything that the intro course for CS majors did. But the course for CS majors didn’t cover everything that MediaComp did. In fact, after a few years, the CS instructors complained that our CS majors didn’t know about RGB and how to implement photo effects (like how to negate an image, or how to generate grayscale from a color picture) — which non-CS majors did know! Content on media got added to the CS majors classes.
Computational Media isn’t CS lite. It’s CS different. The one course that’s different between CS and CM is the required course on computer organization. CS majors take a course based on Patt and Patel’s book. CM majors take a course where they program a Nintendo Gameboy. The courses are not exactly the same, but have a significant overlap. We did a study of the two courses a few years ago and published a journal paper on it (see link here, and article is on my papers page). There was no significant difference in student learning between the two courses. But the CM majors liked their course much more. Now, there are projects on programming the Gameboy in the CS majors classes, too.
Different is good. Different is where you invent new things. Some of those new curricular ideas helped CS courses. Some of those different ideas stayed in the CM and MediaComp courses. Those courses serve different populations and different needs. Not all of it was appropriate or useful for CS majors.
Just because there is difference doesn’t mean that it’s lite. Do we call mechanical engineering “physics lite”? Or chemical engineering “chemistry lite”? I’m sure that there are people who do, but that’s disparaging to the difference and diminishes the value of exploring different combinations of subject areas. Valuing different combinations with computing is a particularly important idea for computer science, because interdisciplinary computing degrees are the only ones where the percentage of women majors are growing (see RESPECT report here). We should value interdisciplinary courses and programs because it’s good for our students and for diversity. We should not disparage the CS + X perspectives as “CS lite.”
The New York Times weighs in on the argument about active learning versus passive lecture. The article linked below supports the proposition that college lectures unfairly advantage those students who are already privileged. (See the post about Miranda Parker’s work for a definition of what is meant by privilege.)
The argument that we should promote active learning over passive lecture has been a regular theme for me for a few weeks now:
- I argued in Blog@CACM that hiring ads and RPT requirements should be changed explicitly to say that teaching statements that emphasize active learning would be more heavily weighted (see post here).
- The pushback against this idea was much greater than I anticipated. I asked on Facebook if we could do this at Georgia Tech. The Dean of the College of Engineering was supportive. Other colleagues were strongly against it. I wrote a blog post about that pushback here.
- I wrote a Blog@CACM post over the summer about the top ten myths of computing education, which was the top-visited page at CACM during the month of July (see post here). I wrote that post in response to a long email thread on a College of Computing faculty mailing list, where I experienced that authority was able to sway CS faculty more than research results (blog post about that story here).
The NYTimes piece pushes on the point that this is not just an argument about quality of education. The argument is about what is ethical and just. If we value broadening participation in computing, we should use active learning methods and avoid lecture. If we lecture, we bias the class in favor of those who have already had significant advantages.
Thanks to both Jeff Gray and Briana Morrison who brought this article to my attention.
Yet a growing body of evidence suggests that the lecture is not generic or neutral, but a specific cultural form that favors some people while discriminating against others, including women, minorities and low-income and first-generation college students. This is not a matter of instructor bias; it is the lecture format itself — when used on its own without other instructional supports — that offers unfair advantages to an already privileged population.
The partiality of the lecture format has been made visible by studies that compare it with a different style of instruction, called active learning. This approach provides increased structure, feedback and interaction, prompting students to become participants in constructing their own knowledge rather than passive recipients.
Research comparing the two methods has consistently found that students over all perform better in active-learning courses than in traditional lecture courses. However, women, minorities, and low-income and first-generation students benefit more, on average, than white males from more affluent, educated families.
The Wall Street Journal recently ran an article by an entrepreneur about why he doesn’t want to hire computer science majors at his start-up. I was particularly struck by this line:
The thing I look for in a developer is a longtime love of coding—people who taught themselves to code in high school and still can’t get enough of it.
Allow me to translate: “I want rich boys. I want those boys who were in the 10% of schools that have CS teachers (which are all rich districts), or parents who knew enough to provide their boys instruction and access from their teen years on. (Nobody really ‘teaches themselves to code.’) The females who start coding in high school will be filtered out by the time I’d hire them. I want those boy to come to me with a decade of immersion in the existing male-dominant, defensive, homogeneous culture that pervades CS classes, so that my startup will be just as lacking in diversity and just as unwelcoming to women. Let’s hear it for the status quo!”
Nathan Ensmenger has not only written a fascinating book about how computing became so male (see book link here), he also maintains a blog that updates the story. The quote and picture below is from a recent post about a recently discovered source that describes women in computing from the 1960’s, back when women were considered better programmers than men. The rhetoric about women being more “sensitive” reminds me of Karen Ashcraft’s plenary talk at NCWIT which I highly recommend (see link here). The story about the Miss USA winner who became a computer programmer is particularly striking.
The Bodony story is not an isolated incident. The book is full of stories from women, and in fact includes an entire chapter devoted to women in computing (“The Equal Sex”). Seligsohn goes so far as to suggest that female programmers are not only equal in ability to men, but superior:
Given a complex customer problem, a female analyst/programmer will often handle the problem better than would her male colleagues with equivalent experience and ability. Not because businessmen are more lenient or show favoritism toward the female of the species, but because the female is often more sensitive to the nuances of a problem and to the complex interpersonal relations that may be part of the problem. In a very real sense, every computer problem with a customer is also a customer relations problem, and this is where feminine tact, insight, and intuition, combining with solid programming and analytical ability, can really pay off for the girl programmer.
Terrific insight from my colleague Charles Isbell. Since school rankings matter so much in faculty hiring, you only have to change things at a few schools to change the field. We could broaden participation in CS PhD’s much more easily than you might think.
Professor Charles Isbell of Georgia Tech delivered an “aha moment” for me. More than 60 percent of the faculty at the top 4, 10, 20, and 25 computer science programs are graduates of one of these same programs. The ranking of one’s PhD institution is a huge factor in hiring—departments hire at their own rank or higher. This is common knowledge, but Charles connected it to diversity. If the very top programs would make a truly concerted effort to increase the participation of women and minorities in PhD programs, the effect would propagate throughout the entire computer science field. Only a few people, those who lead and serve on the PhD admissions committees, can make it happen.
I posted a few weeks about our two Georgia Tech papers at the first ever RESPECT conference (post on Miranda Parker’s paper and post on Barbara Ericson’s paper). The conference itself was great — I expect to see a lot more good things coming out of that conference. (The papers should show up in the IEEE Xplore library soon.)
What I liked about RESPECT was that the focus just on broadening participation in computing issues allowed for greater depth and nuance than at ICER or SIGCSE. The first paper of the day was Representation of Women in Postsecondary Computing 1990-2013: Disciplines, Institutional, and Individual Characteristics Matter by Stuart Zweben and Betsy Bizot. They dove into the differences between women in Computer Science vs. Computer Engineering vs. Software Engineering vs… They all have a depressing downward trend — except for one. Interdisciplinary degrees (like our Computational Media major) are the ones in which representation of women is increasing. (The slide they presented with this graphic was easier to read than the one in the paper, but my picture of the slide is less clear.)
I also found fascinating the paper by Hodari, Ong, Ko, and Smith, Enabling Courage: Agentic Strategies of Women of Color in Computing. They pointed out differences in the experiences of women of color. I was quite surprised at how different they are. (The below graph isn’t in the paper, so you’ll have to make do with my picture of the slide.) That relatively flat red line at the bottom is the percentage of Hispanic or Latina Females in computer science. I found the flatness of that line encouraging. In the last few years, we’ve had a massive rise in enrollment. The fact that the Hispanic/Latina women line is pretty steady means that we must have had a commensurate rise in the numbers of Hispanic/Latina women in CS.
There were a bunch of short papers and lightning talks that left me wanting more detail — which is exactly what they’re supposed to do. The paper Encouraging Online Contributions in Underrepresented Populations by Nacu, Martin, Sandherr, and Pinkard got me thinking about the importance of co-design (involving the target student populations involved in the creation of the classes, like the participatory design methods that Betsy DiSalvo uses) to get buy-in and to insure that the interventions are culturally appropriate.
The RESPECT panels didn’t work as well for me — and I admit to being on one of the two panels. They were more like a bunch of short presentations, and went on too long with little discussion. It’s hard to get panels to work in a research conference. Everybody wants to talk about their thing. Panels work best when there is some disagreement on the panel, and the discussion can help everyone to gain a new perspective.
RESPECT was popular which led to a minor problem. The exemplary paper sessions were packed with all the RESPECT attendees and all the co-located STARS attendees who wanted to hear the great research results! They’re going to need a bigger space next year. That’s a good problem to have for a first time conference.