When women were considered better programmers than men

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.

via The Computer Boys Take Over | Computers, Programmers, and the Politics of Technical Expertise.

New Google for Education report on K-12 CS Ed: Parents want it, Principals don’t get it

Google has just released a new report on K-12 CS Education.  It’s linked at the bottom.  I’m going to quote from a new Wired article that describes one of the big bottomlines.

In a big survey conducted with Gallup and released today, Google found a range of dysfunctional reasons more K-12 students aren’t learning computer science skills. Perhaps the most surprising: schools don’t think the demand from parents and students is there.

Google and Gallup spent a year and a half surveying thousands of students, parents, teachers, principals, and superintendents across the US. And it’s not that parents don’t want computer science for their kids. A full nine in ten parents surveyed viewed computer science education as a good use of school resources. It’s the gap between actual and perceived demand that appears to be the problem.

via Huh? Schools Think Kids Don’t Want to Learn Computer Science | WIRED.

Searching for Computer Science: Access and Barriers in U.S. K-12 Education

To understand perceptions of computer science and associated opportunities, participation, and barriers, we worked with Gallup, Inc. to survey over 1,600 students, 1,600 parents, 1,000 teachers, 9,600 principals, and 1,800 superintendents. We found:

• Exposure to computer technology is vital to building student confidence for computer science learning.

• Opportunities to learn computer science at schools is limited for most students. When available, courses are not comprehensive.

• Demand for CS in schools is high amongst students and parents, but school and district administrators underestimate this interest.

• Barriers to offering computer science in schools include testing requirements for other subjects and limited availability and budget for qualified teachers.

via Google for Education: Computer Science Research.

Want to change the demographics of CS PhDs? You only have to change a handful of schools

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.

via 2015 Faculty Summit informs and inspires – Microsoft Research Connections Blog – Site Home – MSDN Blogs.

First RESPECT Conference: Differences between computing fields and enrollment for women of color

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.

 

Interesting Pushback Against Incentivizing Active Learning in CS Classes

My Blog@CACM post this month makes a concrete proposal (quoted and linked below). We (all academic computing programs) should incentivize faculty to use active learning methods by evaluating teaching statements for hiring, tenure, and promotion more highly that reference active learning and avoid lecture.

On my Facebook page, I linked to the article and tagged our Dean of Engineering, the Vice-Provost for Undergraduate Education, and the RPT Chair for our College, and asked, “Can we do this at Georgia Tech?”  The pushback on my Facebook page was the longest thread I’ve ever been part of on Facebook.

The issues raised were interesting and worth discussing:

• Would implementing this put at a disadvantage new PhD’s who have no teaching experience and don’t learn about active teaching?  Yes, but that incentivizes those PhD programs to change.
• My blog post title is “Be It Resolved: Teaching Statements must embrace Active Learning and eschew Lecture.”  I chose the word “eschew” deliberately.  It doesn’t mean “ban.” It means “deliberately avoid using” which is what I meant.  Lecture has its place — I wrote a blog post defending lecture which still gets viewed pretty regularly.  The empirical evidence suggests that we should use active learning more than lecture for undergraduate STEM education.
• Should such a requirement for teaching statements emerge from faculty talking about it, or should it be done by administrative fiat?  I lean toward the latter.  As I’ve pointed out, CS faculty tend to respond to authority more than evidence. The administration should do the right thing, and deal with educating teachers (e.g., what are active learning methods first? how do we use them? even in large classes?) later. Faculty will learn the active learning methods in order to create those teaching statements.  The incentive comes first.
• Lots of respondents thought I was saying that we should require all teaching to be active learning. I wasn’t, and I don’t know how to enforce that anyway.  By evaluating teaching statements more heavily that emphasize active learning, we create an incentive, not a requirement.
• Some faculty pushed back, “How about students that like lecture? Tough luck for them?” Since we know that active learning is better, even for students who like lecture — yes.
• Several respondents suggested that active learning is just too hard, that faculty are over-stressed as it is.  Faculty are over-stressed, but active learning isn’t that hard.  In fact, it’s hard for faculty because they have to be quiet and listen in class more.  It is hard to make change, but that’s the point of incentives.  We start somewhere.
• The biggest theme in the thread is that we should first aim to get faculty to care about teaching and to take active steps to improve their teaching.  I don’t think that’s enough.  Libertarian paternalism (see Wikipedia page) suggests that we set the incentive at the minimal acceptable level (use of active learning) then encourage choice above that (choosing among the wide variety of active learning methods).  We don’t want people to choose options that won’t be in the best interests of the largest number of people.

The discussion went on for four days (and hasn’t quite petered out yet).  I do wonder if active learning methods will be forced upon faculty if we don’t willingly pick them up.  The research evidence is overwhelming, with articles in Nature and hundreds of studies reviewed in the Proceedings of the National Academy of Sciences.  How long before we get sued for teaching but not using the best teaching methods?  One of the quotes in the blog post says, “At this point it is unethical to teach any other way.” We should take concrete steps towards doing the right thing, because it’s the right thing to do.

Here is something concrete that we in academia can do. We can change the way we select teachers for computer science and how we reward faculty.

All teaching statements for faculty hiring, promotion, and tenure should include a description of how the candidate uses active learning methods and explicitly reduces lecture.

We create the incentive to teach better.  We might simply add a phrase to our job ads and promotion and tenure policies like, “Teaching statements will be more valued that describe how the candidate uses active learning methods and seeks to reduce lecture.”

via Be It Resolved: Teaching Statements Must Embrace Active Learning and Eschew Lecture | blog@CACM | Communications of the ACM.

Technology Can Make a Better World: But Not Technology Alone

NYTimes recently had a series of op-ed articles about the role of technology in our world, specifically, “Is Silicon Valley Saving the World, or Just Making Money?” The piece by Melinda Gates (quoted below) caught my attention because she’s invoking the desire to meet students’ “different learning styles” (see blog post on this theme, and why it leads to worse learning).

There’s an important issue here (beyond me critiquing Melinda Gates, who does important work that I admire). It’s not all technology. We need other disciplines as well. Educational psychologists should be informing these developers at Facebook to tell them, “Stop. That’s a bad idea.”

I was at a workshop last year at Stanford about how to grow more CS Education Research in the United States. Andrew Ng spoke to us about the research going on at Coursera. He was clearly not previously informed about the focus of the workshop. When asked, “Would you want to hire more PhD’s in CS Education?” he answered (my paraphrase), “Sure, but we just hire CS PhD’s, and they’re smart enough to pick up anything on-the-fly.” No, that’s wrong. CS is not a superset of all other disciplines.  That belief is exactly the problem I see in the below quoted piece. Scholars in other areas do know things that CS PhD’s don’t, and they bring something unique to the table. Believing that it’s all technology is exactly why Silicon Valley gets accused of being more interested in money than having actual positive impact.

One of the biggest problems in American education is that teachers have to teach 30 students with different learning styles at the same time. Developers at Facebook, however, have built an online system that gives teachers the information and tools they need to design individualized lessons. The result is that teachers can spend their time doing what they’re best at: inspiring kids.

via Technology Can Make a Better World, If We Want It To – NYTimes.com.

ICER 2015 Report: Blocks win–Programming Language Design == UI Design

ICER 2015 at the University of Nebraska, Omaha was fantastic.  Brian Dorn did a terrific job hosting all of us.

The Doctoral Consortium went really well.  We had 20 students from US, Chile, Germany, and UK.  Below is a picture from the “Up against the wall bubble sort” where experienced students went to one side, and newer students went to the other, and the former gave advice to the latter.

 

 

Georgia Tech had even more going on at ICER and RESPECT than I mentioned in my earlier blog posts (like here and here).  The GVU Center did a nice write up about all of us here.  The biggest thrill at ICER for the GT crowd was Briana Morrison receiving the Chairs Award (one of two best paper awards at ICER) for the paper that I blogged about here.  Below is the whole GT contingent at ICER (including chair Brian Dorn, GT alum).

The other best paper award, the peoples’ choice John Henry Award, went to Kristin Searle and Yasmin Kafai (see paper here) about the e-textiles work with American Indians that I blogged about here.  Kristin had so many interesting insights, like the boys in her project telling her that “I don’t own” the projects they made because they felt no ownership over the programming environment they were using.

The quality of the papers was very good (you can see the list of all of them here).  My favorite paper from my review packet was presented Monday morning, Spatial Skills in Introductory Computer Programming.  Steve Cooper and Sheryl Sorby with two undergraduates at Stanford did the study that I’ve been wanting to see for ages (see blog post where I talk about it). Training an experimental group in spatial skills improved performance over a control group.  Surprisingly, SES and race differences disappeared in the experimental group!  This is an important result.

But one session blew me away — it changed how I think about blocks programming.

• The first paper was from Thomas Price and Tiffany Barnes showing that students using blocks were able to achieve programming tasks faster than those using text, but with no difference in learning or attitudes afterwards (paper here).  This was an interesting result, but it was a limited study (short intervention, no pre-test) so it mostly supported a finding from Chris Hundhausen from years previous that graphical, direct-manipulation languages lead to faster start-up than text languages (see paper here).
• David Weintrop presented his remarkable paper with Uri Wilensky (see paper here).  Below is the graph that changed my thinking about blocks.  David carefully developed an isomorphic test in blocks and text, and gave it to the same population.  Students did much better on the blocks-based test. MODALITY MATTERS!  Blocks and text are not equivalent. He did careful analyses at each level of the test. For example, David replicated the result that else clauses in text are really hard for novices (which I talked about here), but students perform much better in blocks-based if-else.

 

 

• Diana Franklin presented their paper describing fourth graders reading Scratch programs (see paper here).  I was expecting a paper on program comprehension — it wasn’t.  Instead, it was a paper about user interfaces, and how the user interface interfered or supported students exploring and coming to understand the program.

I came away from that three papers realizing that blocks programming is likely the best modality to use in elementary school programming, and perhaps even when starting to program in high school, and maybe even for end-user programmers.  But even more important, I realized that Amy Ko’s comments about programming languages as being a powerful and unusable user interface (see her blog post here) is the critical insight about programming today.  David showed us that blocks can dramatically increase readability of programs.  Diana showed us that the user interface dramatically influences the readability of the blocks.  At the novice programming level, blocks-based languages are the most promising direction today, and designing good blocks languages is as much a user interface design problem as it is a programming language design problem.

 

Making Makers in Nedlam’s Workshop

Nice piece about the Makerspace that Ben Shapiro helped create to draw Haitian girls into STEM.  Ben’s quote is great — it’s about changing attitudes and identities, not just teaching tech.

The tools, computers and gadgets in Nedlam’s Workshop might imply that the Makerspace’s main purpose is to teach students technical skills, but that’s just one benefit it provides.

“At the heart of it, it’s not so much about any specific technology as how we can develop kids’ identity as people who can do this kind of stuff,” Shapiro said. “And how we can change the perception of teachers and administrators in the process.”

Demographically, young white males populate most Makerspaces in the United States, but Nedlam’s Workshop is used mostly by Haitian girls–recent U.S. immigrants, many of whom do not speak English fluently, and struggle with traditional classes. Nedlam’s Workshop offers an alternative mode of learning and expression.

via National Science Foundation (NSF) Discoveries – Making Makers in Nedlam’s Workshop | NSF – National Science Foundation.

RESPECT 2015 Preview: Project Rise Up 4 CS

Our second RESPECT paper is by Barbara Ericson and Tom McKlin. Barb has been developing this cool intervention based on the Texas-based Advanced Placement Incentive Program (APIP) from AP Strategies (see paper about that work) and Betsy DiSalvo’s Glitch (see blog post here). Barb is offering financial incentives to African American students to encourage them to take advantage of additional learning opportunities (e.g., attend webinars and face-to-face workshops), and then pass the AP CS exam. NMSI has also offered grants to states to replicate the Texas APIP project (e.g., blog post at NMSI).

Barb has published on Project Rise Up 4 CS at SIGCSE (see my blog post on that paper). This new paper, “Helping African American Students Pass Advanced Placement Computer Science: A Tale of Two States,” describes Barb’s efforts to replicate the project in another state, and Tom’s efforts to measure what happened.

The bottomline is that in both states where she tried this, the participants had significant improvements in attitudes towards computing from pre to post. Probably the most important attitude change is that the participants had a significant increase in their perceived ability to pass the exam, and some of the students said that they couldn’t have passed the AP CS exam without Project Rise Up 4 CS. Both states had their highest-ever African American AP CS pass rates, though it would be hard to ever make a causal argument that this is due to Project Rise Up 4 CS.

The significant contribution of the paper is the deep understanding of what the project meant to the students, based on interviews. Students talked about their classes and teachers, what worked in Project Rise Up 4 CS, and how the project helped their confidence and knowledge. Barb used undergraduate students to host the webinars and workshops, who served as “near-peer” mentors and role models for the students. Those near-peer mentors were a critical piece in making Project Rise Up work.

Barb’s paper is being highlighted as one of four “Exemplary” papers at RESPECT.

RESPECT 2015 Preview: The Role of Privilege in CS Education

On Friday, August 14, the first RESPECT conference will be held in Charlotte, NC — the first international meeting of the IEEE Special Technical Community on Broadening Participation with technical co-sponsorship by the IEEE Computer Society (see conference website here). RESPECT stands for Research on Equity and Sustained Participation in Engineering, Computing, and Technology.

We have two papers in RESPECT which I’ll summarize in a couple of blog posts. I’m less familiar with IEEE rules on paper referencing and publishing, so I’ll make a copy available as soon as I get the rules sorted out.

Miranda Parker has just finished her first year as a Human-Centered Computing PhD student at Georgia Tech, working with me. She’s done terrific work in her first year which I hope to be talking more about as she publishes. At RESPECT 2015, she’ll be presenting her first paper as a PhD student, “A critical research synthesis of privilege in computing education.”

Miranda defines privilege as:

Privilege is an unearned, unasked-for advantage gained because of the way society views an aspect of a student’s identity, such as race, ethnicity, gender, socioeconomic status, and language.

Her short paper is a review of the literature on how we measure privilege, where its impact has been measured in other STEM fields, and where there are holes in the computing education literature. She’s using studies of privilege in other STEM fields to help define new research directions in computing education. It’s just the sort of contribution you’d want a first year PhD student to make. She’s surveying literature that we don’t reference much, and using that survey to identify new directions — for her, as well as the field.

ICER 2015 Preview: Subgoal Labeling Works for Text, Too

Briana Morrison is presenting the next stage of our work on subgoal labeled worked examples, with Lauren Margulieux. Their paper is “Subgoals, Context, and Worked Examples in Learning Computing Problem Solving.” As you may recall, Lauren did a terrific set of studies (presented at ICER 2012) showing how adding subgoal labels to videos of App Inventor worked examples had a huge effect on learning, retention, and transfer (see my blog post on this work here).

Briana and Lauren are now teaming up to explore new directions in educational psychology space and new directions in computing education research.

• In the educational psychology space, they’re asking, “What if you make the students generate the subgoal labels?” Past research has found that generating the subgoal labels, rather than just having them given to the students, is harder on the students but leads to more learning.
• They’re also exploring what if the example and the practice come from the same or different contexts (where the “context” here is the cover story or word problem story). For example, we might show people how to average test grades, but then ask them to average golf scores — that’s a shift in context.
• In the computing education research space, Briana created subgoal labeled examples for a C-like pseudocode.

One of the important findings is that they replicated the earlier study, but now in a text-based language rather than a blocks-based language. On average, subgoal labels on worked examples improve performance over getting the same worked examples without subgoal labels. That’s the easy message.

The rest of the results are much more puzzling. Being in the same context (e.g., seeing averaging test scores in the worked examples, then being asked to average test scores in the practice) did statiscally worse than having to shift contexts (e.g., from test scores to golf scores). Why might that be?

Generating labels did seem to help performance. The Generate group had the highest attrition. That make sense, because increased complexity and cognitive load would predict that more participants would give up. But that drop-our rate makes it hard make strong claims. Now we’re comparing everyone in the other groups to only “those who gut it out” in the Generate group. The results are more suspect.

There is more nuance and deeper explanations in Briana’s paper than I’m providing here. I find this paper exciting. We have an example here of well-established educational psychology principles not quite working as you might expect in computer science. I don’t think it puts the principles in question. It suggests to me that there may be some unique learning challenges in computer science, e.g., if the complexity of computer science is greater than in other studies, then it’s easier for us to reach cognitive overload. Briana’s line of research may help us to understand how learning computing is different from learning statistics or physics.

ICER 2015 Preview: First CSLearning4U Ebook Paper

ICER 2015 (see website here) is August 9-13 in Omaha, Nebraska. The event starts for me and Barbara Ericson, Miranda Parker, and Briana Morrison on Saturday August 8. They’re all in the Doctoral Consortium, and I’m one of the co-chairs this year. (No, I’m not a discussant for any of my students.) The DC kickoff dinner is on Saturday, and the DC is on Sunday. My thanks to my co-chair Anthony Robins and to our discussants Tiffany Barnes, Steve Cooper, Beth Simon, Ben Shapiro, and Aman Yadav. A huge thanks to the SIGCSE Board who fund the DC each year.

We’ve got two papers in ICER this year, and I’ll preview each of them in separate blog posts. The papers are already available in the ACM digital library (see listing here), and I’ll put them on my Guzdial Papers page as soon as the Authorizer updates with them.

I’m very excited that the first CSLearning4U project paper is being presented by Barbara on Tuesday. (See our website here, the initial blog post when I announced the project here, and the announcement that the ebook is now available). Her paper, “Analysis of Interactive Features Designed to Enhance Learning in an Ebook,” presents the educational psychology principles on memory and learning that we’re building on, describes features of the ebooks that we’re building, and presents the first empirical description of how the Runestone ebooks that we’re studying (some that we built, some that others have built) are being used.

My favorite figure in the paper is this one:

This lists all the interactive practice elements of one chapter of a Runestone ebook along the horizontal axis (in the order in which they appear in the book left-to-right), and the number of users who used that element vertically. The drop-off from left-to-right is the classic non-completion rate that we see in MOOCs and other online education. Notice the light blue bars labelled “AC-E”? That’s editing code (in executable Active Code elements). Notice all the taller bars around those light blue bars? That’s everything else. What we see here is that fewer and fewer learners edit code, while we still see learners doing other kinds of learning practice, like Parsons Problems and multiple choice problems. Variety works to keep more users engaged for longer.

A big chunk of the paper is a detailed analysis of learners using Parsons Problems. Barbara did observational studies and log file analyses to gauge how difficult the Parsons problems were.  The teachers solved them in one or two tries, but they had more programming experience.  The undergraduate and high schools students had more difficulty — some took over 100 tries to solve a problem. Her analysis supports her argument that we need adaptive Parsons Problems, which is a challenge that she’s planning on tackling next.

Why we are teaching science wrong, and how to make it right: It’s about CS retention, too

Important new paper in Nature that makes the argument for active learning in all science classes, which is one of the arguments I was making in my Top Ten Myths blog post. The image and section I’m quoting below are about a different issue than learning — turns out that active learning methods are important for retention, too.

Active learning is winning support from university administrators, who are facing demands for accountability: students and parents want to know why they should pay soaring tuition rates when so many lectures are now freely available online. It has also earned the attention of foundations, funding agencies and scientific societies, which see it as a way to patch the leaky pipeline for science students. In the United States, which keeps the most detailed statistics on this phenomenon, about 60% of students who enrol in a STEM field switch to a non-STEM field or drop out2 (see ‘A persistence problem’). That figure is roughly 80% for those from minority groups and for women.

via Why we are teaching science wrong, and how to make it right : Nature News & Comment.