Developer Bootcamps and Computing Education: Tech Done Right Podcast

I was so excited to be invited to do this podcast with Noel Rappin (my first PhD student) and Jeff Casimir who runs the Turing Academy bootcamp. I learned a lot about bootcamps from Jeff, whom I was pleased to learn is a data geek and measures things pretty carefully.  Two of my favorite insights:

• Female students are more likely to graduate from the bootcamp. They are more likely than male graduates to leave before six months on the job.
• Students who skip college and go straight to bootcamp (as Peter Thiel encourages students to do) have a harder time graduating and getting a job. That latter part might be ageism, bias against younger job-seekers.

I recommend the podcast — we had a fun discussion.

How do people learn computing? Who learns best from traditional computer science education and who from bootcamps? How can we teach people who are not developers but who need to learn some programming to do their jobs? Jeff Casimir, the founder of Turing academy, and Georgia Tech’s Mark Guzdial, one of the founders of the International Computing Education Research conference, join Noel to answer these questions and also explain why Excel is both the best and the worst thing in the world.

Source: Tech Done Right Episode 20: Developer Bootcamps and Computing Education with Jeff Casimir and Mark Guzdia

White House announces $200 million a year for computer science – Code.org #CSforAll

Looking forward to hearing more details at Code.org’s webinar this afternoon.  Hadi Partovi posted on Facebook that the money will be provided as competitive grants to schools and non-profits through the Department of Education.  Hadi has written a personal blog post about his motivations in supporting this announcement.

The White House memorandum on the announcement is here. I don’t understand all the details here, and the details of the funding are important.  If it’s not new funding, then it puts CS in competition with other fields, e.g., if the money is set aside for CS when it was originally allocated for all of STEM.  The White House memorandum says, “Establish promotion of high-quality STEM education, with a particular focus on Computer Science, as a Department of Education priority.” If it’s a preference (e.g., a school gets money if and only if they’re teaching CS), it may hurt schools that can’t afford to teach CS yet because they’re stretched thin teaching literacy and mathematics.

Here’s the webinar information: (9/26) at 11am PT, 2pm ET
By web: https://code.zoom.us/j/783490509
By phone: US: +1 646 558 8656 or +1 669 900 6833
Webinar ID: 783 490 509

Today, the White House announced a $200 million per year commitment to computer science education in America’s schools. Unlike similar proposals in previous years, today’s action delivers funding to schools, immediately. Besides expanding access to computer science in schools that previously didn’t teach it, the funds promise to increase participation by women and underrepresented minorities.This funding will jumpstart efforts to ensure every student in every school has the opportunity to learn computer science as part of a well-rounded education. For advocates of increased access and diversity in CS, this is the culmination of years of momentum that began in classrooms, spread to entire school districts, and won the support of business leaders and elected officials globally.

Source: $200 million a year for computer science – Code.org – Medium

The Negative Consequences of Brown v Board of Education: Integrating Computing Education

The second season of Revisionist History has just finished.  This season didn’t have the same multiple episodes with tight ties to the issues of education as last season (as I described in this blog post), there was one standout episode that does relate to our issues: Miss Buchanan’s Period of Adjustment.  The podcast deals with the negative consequences of the Brown v Board of Education Supreme Court case that declared that separate was not equal and forced schools to integrate.  The well-documented consequence of the integration was the closing of the schools for African-Americans and the firing of Black school teachers.  Gladwell first considers what the Brown family (named in the case) and the other families in the case actually wanted, and about the longterm impact that even today, there are disproportionately few African-American teachers in the US are African-American — and that leads to impacts on students.

When I studied Brown v Board of Education when I was a graduate student at the University of Michigan, we were taught a negative consequence that Gladwell barely touches on.  Gladwell mentions that there were few jobs for an educated Black person at the time of Brown v Board.  The Supreme Court’s decision, and the consequent firing of Black teachers, was an enormous blow to the African-American middle class in the United States.  Employment was lost at a large scale, and longterm impacts on wealth and prosperity can be measured today.

The connection to computer science education is part of the question of how do we reach everyone and help everyone to succeed.  Today’s computing education is de facto segregated — not in the sense of colored vs white classes, but in terms of only certain demographics are in CS classes and other demographics are not.

• In many of the high schools we work with, even if white and Asian students are in the school population minority, the computer science classes are mostly white and Asian.
• English CS classes are almost entirely male, maybe even more than in the US (described here).
• US undergraduate CS classes don’t seem to be retaining women (blog post here).
• Code.org classes have are almost half poor students (blog post here), and have excellent diversity (see their Medium post here). What are the rich students taking?  The diversity that Code.org is seeing is not reflected in undergraduate CS (see Generation CS report) which has little diversity and has mostly prosperous students. That’s important because undergraduate CS is the path that most students will take to the IT industry, which is mostly white/Asian and male.

How do we improve diversity in computing education?  Can we avoid a heavy-handed and expensive mandate like requiring CS for everyone? I side effect of requiring everyone to take CS might be that we get all the same kind of CS.  Can we provide equal access to everyone without the negative consequences that Gladwell describes from Brown v Board of Education?

Brown v Board of Education might be the most well-known Supreme Court decision, a major victory in the fight for civil rights. But in Topeka, the city where the case began, the ruling has left a bittersweet legacy. RH hears from the Browns, the family behind the story.

Source: Revisionist History Podcast

The Father Of Mobile Computing Is Not Impressed: The Weight of Redefining the Normal

I have been fortunate to have heard Alan Kay talk on the themes in this interview many times, but either he’s getting better at it or I’m learning enough to understand him better, because this was one of my favorites. (Thanks to Ben Shapiro for sending it to me.)  He ties together Steve Jobs, Neal Postman, and Maria Montessori to explain what we should be doing with education and technology, and critiques the existing technology as so much less than what we ought to be doing.  In the quote below, he critiques Tim Berners-Lee for giving us a World Wide Web which was less than what we already knew how to do.  The last paragraph quoted below is poignant: It’s so hard to fix the technology once it’s established because of “the weight of this redefining of the normal.”

What I understood this time, which I hadn’t heard before, was the trade-off between making technology easier and making people better.  I’ve heard Alan talk about using technology to improve people, to help them learn, to challenge their thinking.  But Alan led the team that invented the desktop user interface — he made computing easier.  Can we have both?  What’s the balance that we need? That’s where Neal Postman and Bertrand Russel come in, as gifted writers who drew us in and then changed our minds. That’s why we need adults who know things to create a culture where children learn 21st century thinking and not oral culture (that’s the Maria Montessori part), and why the goal should be about doing what’s hard — not doing what’s universal, not doing what pre-literate societies were doing.  Alan critiques the iPhone as not much better than the television for learning, when the technology in the iPhone could have made it so much more.

He tosses out another great line near the end of the interview, “How stupid is it, versus how accepted is it?”  How do we get unstuck?  The iPhone was amazing, but how do we roll back the last ten years to say, “Why didn’t we demand better? How do we shuck off the ‘the weight of this redefining of the normal’ in order to move to technology that helps us learn and grow?”

And so, his conception of the World Wide Web was infinitely tinier and weaker and terrible. His thing was simple enough with other unsophisticated people to wind up becoming a de facto standard, which we’re still suffering from. You know, [HTML is] terrible and most people can’t see it.

FC: It was standardized so long ago.

AK: Well, it’s not really standardized because they’re up to HTML 5, and if you’ve done a good thing, you don’t keep on revving it and adding more epicycles onto a bad idea. We call this reinventing the flat tire. In the old days, you would chastise people for reinventing the wheel. Now we beg, “Oh, please, please reinvent the wheel.”At least give us what Engelbart did, for Christ’s sake.

But that’s the world we’re in. We’re in that world, and the more stuff like that world that is in that world, the more the world wants to be that way, because that is the weight of this redefining of the normal.

Source: The Father Of Mobile Computing Is Not Impressed

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

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

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

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

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

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.

• Most obvious is the Chairs Award paper by Danielsiek et al on a measure of self-efficacy, what students believe about their own success in algorithms.
• The next most obvious is the paper out of Michigan State on Students’ Emotional Reactions to Programming Projects in Introduction to Programming: Measurement Approach and Influence on Learning Outcomes
• A paper out of New Zealand looked at what students drew when asked to draw what programming meant to them. Lots of dollar signs in these pictures. (The ‘Art’ of Programming: Exploring Student Conceptions of Programming through the Use of Drawing Methodology.)
• Even James Prather et al’s paper about novice interactions with compiler error messages (definitely a programming language related issue) spent time considering issues of frustration.
• Thomas Price et al’s paper about when students ask for help dealt significantly with the students beliefs about whether a human or a computer tutor was more likely to be able to help them.
• When Brian Dorn presented the paper by Tracie Evans Reding and himself about the teacher experience in CS professional development, he used a roller coaster as a visual metaphor at the start of his talk. It was all about the rise and fall in teacher confidence and beliefs.
• As I mentioned in my earlier blog post, the paper by Thayer and Ko on bootcamps talked about what bootcamp attendees believed going into the camp, their deep frustration with the camp, and the pain of being unable to find a job afterwards.

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 Amy Ko’s summary of ICER 2017.)