Posts filed under ‘Uncategorized’

The challenge of retaining women in computing: The 2016 Taulbee Survey: Supplementary Report on Course-level Enrollment

The Computing Research Association (CRA) has just released a supplement to their 2016 Taulbee Survey report.  They now are collecting individual course data, which gives them more fine-grained numbers about who is entering the major, who is retained until mid-level, and who makes it to the upper-level.  Previously, they mostly just had enrollment and graduation data.  These new data give them new insights.  For example, we are getting more women and URM in computing, but we are not retaining them all.

Except in the introductory course for non-majors, the median percentage of women in courses at each level was either fairly constant or increasing [from previous years]. The most notable increase was in the mid-level course, where the median percentage of women went from 17.4 in 2015 to 20.0 in 2016. The median percentage of women in the upper-level course also increased, from 14.1 to 15.9 percent. We see a slight drop-off from the median percentage of women in the introductory course for majors in 2015 (21.0 percent) to the median percentage of women in the mid-level course in 2016 (20.0 percent), and a somewhat larger drop-off between the median percentage of women in the mid-level course in 2015 (17.4 percent) and the median percentage of women in the upper-level course in 2016 (15.9 percent).  Because the median percentage at each level is for a single representative course, not for all students at that level, some of the differences between levels may be attributable to the specific courses on which the institutions chose to report. Overall, however, this trend of decreasing representation of women at higher course levels is congruent with other data.

Source: The 2016 Taulbee Survey: Supplementary Report on Course-level Enrollment – CRA

September 18, 2017 at 7:00 am 3 comments

British girls “logging off” from CS: What’s the real problem?

The BBC reports (in the article linked below) that the “revolution in computing education has stalled.”  The data from England (including the Roehampton Report, discussed in this blog post) do back up that claim — see the quotes at the bottom.

In this post, I’m reflecting on the response from the British Computer Society. “We need to do more with the curriculum to show it’s not just a nerdy boys’ subject. We’ve got to show them it’s about real problems like climate change and improving healthcare.”  There are some interesting assumptions and warrants in these statements.  Do girls avoid CS because they think it’s a boys’ subject, or because it’s not about real problems?  How does the curriculum “show” that it is (or isn’t) a “nerdy boys’ subject”?  If the curriculum emphasized “real problems,” would it no longer be a “nerdy boys’ subject”?  Are these at all connected? Would making CS be like “climate change and improving healthcare” attract more female students?

First, I’d like to know if the girls choosing ICT over CS are actually saying that it’s because CS is “a nerdy boys’ subject,” and if the girls know anything about the curriculum in CS.  In our research, we found that high school students know very little about what actually happens in undergraduate CS, and undergraduate students in CS don’t even know what’s in their next semester’s classes. Changing the curriculum doesn’t do much good if the girls’ decisions are being made without knowing about the curriculum.  The former claim, that CS is perceived by girls as a “nerdy boys’ subject,” is well-supported in the literature.  But is that the main reason why the girls aren’t enrolling?

Do we know that this a curriculum issue at all? The evidence suggests that there are other likely reasons.

  • Maybe it’s not the curriculum’s “problem” focus, but the “learning objective” focus. Do the girls percieve that the point of the course is to become part of the Tech industry as a professional programmer?  Maybe girls are more interested in broadening their potential careers and not limiting their options to IT?  ICT can be used anywhere.  CS might be perceived as being about being a software developer.
  • Are the girls seeing mass media depictions of programming and deciding that it’s not for them?  A 2016 ICER paper by Colleen Lewis, Ruth Anderson, and Ken Yasuhara explored the reasons why students might not feel that they have a good “fit” with CS (see ACM paper link here).  But are those the reasons why women might not even try CS? Maybe they have had experiences with programming and decided that they didn’t fit? Or maybe the decided that syntax errors and unit tests are just tedious and boring?
  • Are the girls seeing mass media depictions of the Tech industry and deciding that they’d rather not be a Googler or work at Uber? They are probably hearing about things like the Damore memo at Google. Whether they think he’s right or not, maybe girls are saying that they just don’t want to bother.
  • Do the girls have more choices, and CS is simply less attractive in comparison?  It may be that girls know that CS is about solving real problems, but they’d rather solve real problems in law, medicine, or business.
  • Do the girls perceive that wages are not rising in the Tech industry?  Or do the girls perceive that they can make more money (perhaps with fewer negative connotations) as a lawyer, doctor, or businessperson?

I have heard from some colleagues in England that the real problem is a lack of teachers.  I can believe that having too few teachers does contribute to the problem, but that raises the same questions at another level.  Why don’t teachers teach computer science?  Is it because they don’t want to be in the position of being “vocational education,” simply preparing software developers?  Or are teachers deciding that they are dis-interested in software development, for themselves or for their students?  Or are the teachers looking at other areas of critical need for teachers and decide that CS is less attractive?

Bottom line is that we know too little, in the UK or in the US (see Generation CS), about what is influencing student and teacher decisions to pursue or to avoid classes in computing. The reality doesn’t matter here — people make decisions based on their perceptions.

In England, entries for the new computer science GCSE, which is supposed to replace ICT, rose modestly from 60,521 in 2016 to 64,159 this year. Girls accounted for just 20% of entries, and the proportion was a tiny bit lower than last year.

ICT entries fell from 84,120 to 73,099, which you would expect as the subject is disappearing from the national curriculum. But it had proved more attractive to girls. Even there, the proportion of female entries fell from 41% to 39%.

Combine the two subjects, and you find that the number studying either subject has fallen by over 7,000 in the past year. Back in 2015 more than 47,000 girls were getting some kind of computing qualification, and that has fallen to about 41,000 – just 30% of the total.

Source: Computer science: Girls logging off – BBC News

September 15, 2017 at 7:00 am 8 comments

Learning Programming at Scale: Philip Guo’s research

I love these kinds of blog posts.  Philip Guo summarizes the last three years of his research in the post linked below.  I love it because it’s so important and interesting (especially for students trying to understand a field) to get a broad explanation of how a set of papers relate and what they mean.  Blog posts may be our best medium for presenting this kind of overview — books take too long (e.g., I did a book to do an overview of 10-15 years of work, but it may not be worth the effort for a shorter time frame), and few conferences or journals will publish this kind of introspection.

My research over the past three years centers on a term that I coined in 2015 called learning programming at scale. It spans the academic fields of human-computer interaction, online learning, and computing education.

Decades of prior research have worked to improve how computer programming is taught in traditional K-12 and university classrooms, but the vast majority of people around the world—children in low-income areas, working adults with full-time jobs, the fast-growing population of older adults, and millions in developing countries—do not have access to high-quality classroom learning environments. Thus, the central question that drives my research is: How can we better understand the millions of people from diverse backgrounds who are now learning programming online and then design scalable software to support their learning goals? To address this question, I study learners using both quantitative and qualitative research methods and also build new kinds of interactive learning systems.

Source: Learning Programming at Scale | blog@CACM | Communications of the ACM

September 11, 2017 at 7:00 am Leave a comment

Personality Tests Are Fun But Don’t Capture Who You Really Are and Should Not Be Part of Hiring

Annie Murphy Paul has been trying to convince people for years now that personality tests don’t really work — they’re not valid, they’re not reliable, and it’s not clear what they’re measuring.  This issue is important because the Tech industry still believes in tests like these when hiring. (Or so I hear — as a professor, I only know the hiring process from student stories.) They introduce significant bias into hiring. How do we get rid of them?

Twelve years ago, I tried to drive a stake into the heart of the personality-testing industry. Personality tests are neither valid nor reliable, I argued, and we should stop using them — especially for making decisions that affect the course of people’s lives, like workplace hiring and promotion.

But if I thought that my book, The Cult of Personality Testing, would lead to change in the world, I was keenly mistaken. Personality tests appear to be more popular than ever. I say “appear” because — today as when I wrote the book — verifiable numbers on the use of such tests are hard to come by.Personality testing is an industry the way astrology or dream analysis is an industry: slippery, often underground, hard to monitor or measure. There are the personality tests administered to job applicants “to determine if you’re a good fit for the company”; there are the personality tests imposed on people who are already employed, “in order to facilitate teamwork”; there are the personality tests we take voluntarily, in career counseling offices and on self-improvement retreats and in the back pages of magazines (or, increasingly, online).

Source: Personality Tests Are Fun But Don’t Capture Who You Really Are : Shots – Health News : NPR

September 8, 2017 at 7:00 am 1 comment

Google study on the challenges for rural communities in teaching CS

Google continues their series of reports on the challenges of teaching CS, with a new report on rural and small-town communities in the US.  This is an important part of CS for All, and is a problem internationally.  The Roehampton Report found that rural English schools were less likely to have computing education than urban schools.  How do we avoid creating a computing education divide between urban and rural schools?

This special brief from our Google-Gallup study dives into the opportunities and challenges for rural and small-town communities. Based on nationally representative surveys from 2015-16, we found:

  • Students from rural/small-town schools are just as likely as other students to see CS as important for their future careers, including 86% who believe they may have a job needing computer science.

  • Rural/small-town parents and principals also highly value CS, with 83% of parents and 64% of principals saying that offering CS is just as or more important than required courses.

  • Rural/small-town students are less likely to have access to CS classes and clubs at school compared to suburban students, and their parents are less likely to know of CS opportunities outside of school.

  • Rural/small-town principals are less likely to prioritize CS, compared to large-city or suburban principals.

Source: Google for Education: Computer Science Research

September 4, 2017 at 7:00 am 1 comment

The Role of Emotion in Computing Education, and Computing Education in Primary School: ICER 2017 Recap

I wrote my Blog@CACM post in August about the two ICER 2017 paper awards:

  • Danielsiek et al’s development of a new test of student self-efficacy in algorithms classes;
  • Rich et al.’s trajectories of K-5 CS learning, which constitute an important new set of theories about how young students learn computing.

Rich et al.’s paper is particularly significant to me because it has me re-thinking my beliefs about elementary school computer science. I have expressed significant doubt about teaching computer science in early primary grades — it’s expensive, there are even more teachers to prepare than in secondary schools, and it’s not clear that it does any longterm good. If a third grader learns something about Scratch, will they have learned something that they can use later in high school? Katie Rich presented not just trajectories but Big Ideas. Like Big Ideas for sequential programming include precision and ordering. It’s certainly plausible that a third grader who learns that precision and ordering in programs matters, might still remember that years later. I can believe that Big Ideas might transfer (at least, within computing) over years.

I was struck by a recurring theme of emotion in the papers at ICER 2017. We have certainly had years where cognition has been a critical discussion, or objects, or programming languages, or student’s process. This year, I noticed that many of these papers were thinking about beliefs and feelings.

I find this set of papers interesting for highlight an important research question: What’s the most significant issue influencing student success or withdrawal from computer science? Is it the programming language they use (blocks vs text, anyone?), the kind of error messages they see, the context in which the instruction is situated, or whether they use pair programming? Or is the most significant issue what the students believe about what they’re doing? And maybe all of those other issues (from blocks to pairs) are really just inputs to the function of student belief?

(Be sure to check out Andy Ko’s summary of ICER 2017.)

September 1, 2017 at 7:00 am 1 comment

The Problems with Coding Bootcamps: Allure with little Payoff

Audrey Watters weighs in below on why Coding Bootcamps are failing. She argues that bootcamps aren’t filling a real need, that there really isn’t a huge untapped need for coding skills.

Kyle Thayer and Andy Ko just published an article at ICER 2017 about their analyses of bootcamps.  Kyle has a nice summary as a Medium post (see link here), but I recommend reading the actual ICER paper, too.  Kyle’s summary is balanced about the strengths and weaknesses of coding bootcamps, while I think the results in the ICER paper are much more critical.  This one quote, about the nine months (!) following graduation, was particularly compelling for me, “I preŠtty much devoted my time to [my bootcamp’s] prescribed job hunting methods, which means €financially, I have no money. [. . . ] And that [sacrifice] reflects on my family because now we’re low on funds [. . . ] and now instead of selling our house and buying a house, we’re selling our house to pay the debt that we’re in and then go rent until I can €find a job.”

Kyle’s visualization of the paths of his 26 interviewees is rich with detail, but can be confusing.  Here’s a slice of three of them.

What I didn’t get at first is that the gray area to the right is planned (or even imagined).  So P18, above, has already had one partial bootcamp (half-moon), one complete bootcamp, and still doesn’t have the desired job (the star in the upper right hand corner).  Of his 26 interviewees, only three have their desired job in the software industry.  Several have less than desirable jobs (including one that has an unrelated job and gave up). Nine of the 26 had already dropped out of a bootcamp.

When I read Kyle and Andy’s study about the struggle and pain that the bootcamp attendees go through, including difficulties finding jobs beyond what was expected, and then read Audrey’s piece suggesting that there might not be as many jobs available as people think, I wonder what is the allure of bootcamps.  Why go through all of that when there isn’t a guaranteed (or even likely?) payoff?

Within the past week, two well-known and well-established coding bootcamps have announced they’ll be closing their doors: Dev Bootcamp, owned by Kaplan Inc., and The Iron Yard, owned by the Apollo Education Group (parent company of the University of Phoenix). Two closures might not make a trend… yet. But some industry observers have suggested we might see more “consolidation” in the coming months.

It appears that there are simply more coding bootcamps – almost 100 across the US and Canada – than there are students looking to learn to code. (That is to say, there are more coding bootcamps than there are people looking to pay, on average, $11,000 for 12 weeks of intensive training in a programming language or framework).

All this runs counter, of course, to the pervasive belief in a “skills gap” – that there aren’t enough qualified programmers to fill all the programming jobs out there, and that as such, folks looking for work should jump at the chance to pay for tuition at a bootcamp. Code.org and other industry groups have suggested that there are currently some 500,000 unfilled computing jobs, for example. But that number is more invention than reality, a statistic used to further a particular narrative about the failure of schools to offer adequate technical training. That 500,000 figure, incidentally, comes from a Bureau of Labor Statistics projection about the number of computing and IT jobs that will added to the US economy by 2024, not the number of jobs that are available – filled or unfilled – today.

Perhaps instead of “everyone should learn to code,” we should push for everyone to learn how to read the BLS jobs report.

There isn’t really much evidence of a “skills gap” – there’s been no substantive growth in wages, for example, that one would expect if there was a shortage in the supply of qualified workers.

Source: Why Are Coding Bootcamps Going Out of Business?

August 28, 2017 at 7:00 am 4 comments

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