Archive for August, 2017

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

Google report in CACM: Is the U.S. Education System Ready for CS for All?

Jennifer Wang of Google has the Education Viewpoints column in CACM this month, and she reports on data that Google is collecting on systemic issues preventing CS for All.  It’s an important report that I recommend.

Interestingly, we also found that regardless of race/ethnicity or gender, 80% of students who have learned CS said that they learned CS in a class at school, about twice the rate of any other means of learning, including on their own, through afterschool clubs, online, or in any other program outside of school. This data strongly suggests formal education remains the best way to ensure widespread and equitable access to CS learning.

Yet, we found schools faced many barriers to offering CS classes. We asked principals and superintendents why they did not offer CS in their schools and districts. The most commonly cited barriers had to do with lack of qualified teachers and competing demands of standardized test preparation. Lack of qualified teachers was cited by 63% of principals and 74% of superintendents. Not enough funding to train teachers was cited by 55% of principals and 57% of superintendents. The need to devote time to testing requirements was cited by 50% of principals and 55% of superintendents. This indicates computing professionals can play an important role in expanding access to CS by supporting organizations that train teachers and by providing mentoring and resources to teachers and students.

Source: Is the U.S. Education System Ready for CS for All? | August 2017 | Communications of the ACM

August 25, 2017 at 7:00 am Leave a comment

A Threads-using CS major joins GT Faculty: Welcome to Sauvik Das

Threads were a curriculum innovation from Georgia Tech around 2005, that we have studied in some of our research.  Today, we welcome one of the undergraduates who took Threads as faculty into our School of Interactive Computing.  (He officially starts in January, but he’s hanging out at the faculty retreat and meetings with us.) Welcome to Sauvik Das, and I’m so pleased that he wrote this reflective essay about his journey to re-join us.

Threads are specializations in different application areas of Computer Science: for example, embedded systems (e.g., computing embedded in physical systems), media (e.g., computer graphics, games), machine intelligence, etc. The thread that truly made me think was “people”: “where computing meets its users”. Everything I wanted to do with computing, I reflected, was not actually about computing. It was about using computing to create new, better and engaging experiences for the people that used the systems I made.

Source: Beginnings: Old and New – Sauvik Das – Medium

August 21, 2017 at 7:00 am Leave a comment

Teachers are not the same as students, and the role of tracing: ICER 2017 Preview

The International Computing Education Research conference starts today at the University of Washington in Tacoma. You can find the conference schedule here, and all the proceedings in the ACM Digital Library here. In past years, all the papers have been free for the first couple weeks after the conference, so grab them while they are outside the paywall.

Yesterday was the Doctoral Consortium, which had a significant Georgia Tech presence. My colleague Betsy DiSalvo was one of the discussants. Two of my PhD students were participants:

We have two research papers being presented at ICER this year. Miranda Parker and Kantwon Rogers will be presenting Students and Teachers Use An Online AP CS Principles EBook Differently: Teacher Behavior Consistent with Expert Learners (see paper here) which is from Miranda C. Parker, Kantwon Rogers, Barbara J. Ericson, and me. Miranda and Kantwon studied the ebooks that we've been creating for AP CSP teachers and students (see links here). They're asking a big question: "Can we develop one set of material for both high school teachers and students, or do they need different kinds of materials?" First, they showed that there was statistically significantly different behaviors between teachers and students (e.g. different number of interactions with different types of activities). Then, they tried to explain why there were differences.

We develop a model of teachers as expert learners (e.g., they know more knowledge so they can create more linkages, they know how to learn, they know better how to monitor their learning) and high school students as more novice learners. They dig into the log file data to find evidence consistent with that explanation. For example, students repeatedly try to solve Parsons problems long after they are likely to get it right and learn from it, while teachers move along when they get stuck. Students are more likely to run code and then run it again (with no edits in between) than teachers. At the end of the paper, they offer design suggestions based on this model for how we might develop learning materials designed explicitly for teachers vs. students.

Katie Cunningham will be presenting Using Tracing and Sketching to Solve Programming Problems: Replicating and Extending an Analysis of What Students Draw (see paper here) which is from Kathryn Cunningham, Sarah Blanchard, Barbara Ericson, and me. The big question here is: "Of what use is paper-and-pen based sketching/tracing for CS students?" Several years ago, the Leeds' Working Group (at ITiCSE 2004) did a multi-national study of how students solved complicated problems with iteration, and they collected the students' scrap paper. (You can find a copy of the paper here.) They found (not surprisingly) that students who traced code were far more likely to get the problems right. Barb was doing an experiment for her study of Parsons Problems, and gave scrap paper to students, which Katie and Sarah analyzed.

First, they replicate the Leeds' Working Group study. Those who trace do better on problems where they have to predict the behavior of the code. Already, it's a good result. But then, Katie and Sarah go further. For example, they find it's not always true. If a problem is pretty easy, those who trace are actually more likely to get it wrong, so the correlation goes the other way. And those who start to trace but then give up are even more likely to get it wrong than those who never traced at all.

They also start to ask a tantalizing question: Where did these tracing methods come from? A method is only useful if it gets used — what leads to use? Katie interviewed the two teachers of the class (each taught about half of the 100+ students in the study). Both teachers did tracing in class. Teacher A's method gets used by some students. Teacher B's method gets used by no students! Instead, some students use the method taught by the head Teaching Assistant. Why do some students pick up a tracing method, and why do they adopt the one that they do? Because it's easier to remember? Because it's more likely to lead to a right answer? Because they trust the person who taught it? More to explore on that one.

August 18, 2017 at 7:00 am Leave a comment

Teaching Computer Science Is Great, But It’s Not Enough: Calls for Functional Computer Science Literacy

The article quoted below by Florence R. Sullivan & Jill Denner calls for us to go beyond “simply giving more students access.” We need to give them “functional computer science literacy.”  By that phrase, they mean that we need to have students consider ethical and social issues.  That’s not what Andy DiSessa meant when he defined computational literacy, who talked more about using computing to understand the world.  But there may be a more mundane, critical form of literacy than either of these definitions.

Computing classes that emphasize coding over traditional technology literacy (e.g., how to use the computer) are not attracting students in the UK.  The BBC said it frankly, “Computing in schools – alarm bells over England’s classes.” In the UK, even where there is access to computing education, but students aren’t flocking to the classes.  It’s not just a matter of “time, funding, and qualified teachers.” Traditional Information and Communications Technologies classes are more attractive to English students than Computing classes, based on number of students taking GCSE’s.

Massachusetts merged their digital literacy standards into their new computer science standards.  That’s likely going to be the most successful path. We can use digital literacy as a context to introduce some CS, to draw students into CS classes. CS may not be the draw. Literacy is.

There is still much work to do, however. In an ongoing, multiyear study on computer science education conducted by Google and Gallup, researchers found that although students, parents, teachers, and school administrators value computer science, it is still not offered in many schools. This is because of a lack of time, funding, and qualified teachers. Only 25 percent of schools nationwide reported offering a computer science class in 2014-15, and while that number rose to 40 percent in 2015-16, we are still years away from providing sufficient computer science education in all schools.

As educational researchers focused on computer science learning, we welcome the push by more districts to teach the discipline to students. But we believe that our nation’s current conception of computer science education does not go far enough. It is not sufficient to simply give more students access. As computer science continues to expand, we advocate for educators to teach functional computer science literacy, just as the field of science education has spent decades refining an approach to teaching socio-scientific reasoning (which integrates learning science content in the context of real-world issues).

Source: Education Week

August 14, 2017 at 7:00 am Leave a comment

Leslie Lamport tells Computer Scientists to go create ebooks (and other new media)

Yes! Exactly!  That’s why we’re trying to figure out new media for expressing, learning, and talking about computing.

“If you succeed in attaining a position that allows you to do something great, if you do something that really is great, and if you realize that it’s great, there’s still one more hurdle: You have to convince others that it’s great,” he told the graduates. “This will require writing.”

He exhorted graduates in biological physics; chemistry; computational linguistics; computer science; language and linguistics; mathematics and physics to find new modes of communication.

“There must be wonderful ways in which a writer can interact with the reader that no one has thought of yet, ways that will convey ideas better and will make reading fun,” Lamport said. “I want you to go out and invent them.”

Source: Computer scientist Leslie Lamport to grads: If you can’t write, it won’t compute | BrandeisNOW

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

It’s not about Google. Our diversity efforts aren’t working

The sexist “internal memo” from Google has been filling my social media feeds for the last few days. I’m not that excited about it.  Within every organization, there will be some people who disagree with just about any policy.  The enormous screed is so scientifically incorrect that I have a hard time taking it seriously.  

For example, the memo claims that the gap between men and women in CS is due to biology. That can’t be when there are more women than men in CS, especially in the Middle East and Northern Africa.  I saw a great study at NCWIT a few years ago on why programming is seen as women’s work in those parts of the world — it’s detailed work, done inside, sometimes with one other person. It looks like sewing or knitting. When told that programmers were mostly male in the US, the participants reportedly asked, “What’s masculine about programming?”  There’s an interesting take from four scientists who claim that everything that the internal memo says is correct.

The positive outcome from this memo is Ian Bogost’s terrific essay about the lack of diversity in Tech, from industry to higher education. It’s not about Google. It’s that our diversity efforts are having little impact. Ian explains how our problem with diversity is deeply rooted and influences the historical directions of computing. I highly recommend it to you.

These figures track computing talent more broadly, even at the highest levels. According to data from the Integrated Postsecondary Education Data System, for example, less than 3 percent of the doctoral graduates from the top-10 ranked computer science programs came from African American, Hispanic, Native American, and Pacific Islander communities during the decade ending in 2015.

Given these abysmal figures, the idea that diversity at Google (or most other tech firms) is even modestly encroaching on computing’s incumbents is laughable. To object to Google’s diversity efforts is to ignore that they are already feeble to begin with.

Source: A Googler’s Anti-Diversity Screed Reveals Tech’s Rotten Core – The Atlantic

August 9, 2017 at 7:00 am 13 comments

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