Posts tagged ‘NCWIT’

It’s not about “fixing women”! How Lucy Sanders tackles gender inequity: Data, research, humor

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.”

Source: How Lucy Sanders tackles gender inequity: Data, research, humor – The Denver Post

November 23, 2015 at 8:46 am Leave a comment

You Don’t Have to Be Good at Math to Learn to Code – The Atlantic

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.)

Source: You Don’t Have to Be Good at Math to Learn to Code – The Atlantic

November 16, 2015 at 8:10 am 16 comments

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.

Source: Still No African-Americans Taking the AP Computer Science Exam in Nine States – Curriculum Matters – Education Week

November 9, 2015 at 7:28 am 6 comments

Requirements for a Computing-Literate Society: VL/HCC 2105 Keynote

I gave a keynote talk at VL/HCC 2015 (see the program here) on Tuesday morning.  Here is the abstract, the short form outline, and a link to the slides on

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.


October 21, 2015 at 8:13 am 2 comments

What can I do today to create a more inclusive community in CS? Guest Post from Cynthia Lee

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.


  1. Holly Lord and Joanne McGrath Cohoon. “Recruiting and Retaining Women Graduate Students in Computer Science and Engineering,” 2006. ↩︎
  2. Research shows this intervention mitigates stereotype threat. Reduced racial gap by 30%. ↩︎
  3. This sexist trope is something women have been working to expunge from our vocabulary. Unfortunately, still often seen in discussion of UI design.,_your_mother_could_do_it ↩︎
  4. Carol Dweck. “The New Psychology of Success.” 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. ↩︎
  5. Articulating this idea as slope/y-intercept is from Professor John Ousterhout of Stanford. ↩︎
  6. 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. ↩︎
  7. 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: ↩︎
  8. Occasioned by a news item about a panel discussion in Silicon Valley, NYTimes reviews research on women being interrupted when speaking: ↩︎
  9. Jere Brophy and Thomas Good. “Teachers’ communication of differential expectations for children’s classroom performance,” 1970. ↩︎

September 28, 2015 at 8:50 am 4 comments

Different is not Lite: A 2002 Argument Against Media Computation


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.”

September 23, 2015 at 8:22 am 1 comment

Growing evidence that lectures disadvantage underprivileged students

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.

Source: Are College Lectures Unfair? – The New York Times

September 18, 2015 at 8:44 am 4 comments

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