Posts tagged ‘#CS4All’

Why should we teach programming (Hint: It’s not to learn problem-solving)

This is a revision of the original post. Several readers pointed out on Twitter that my original post was insensitive. It read like an attack on Brenda, a woman of color, from a senior white guy (me). That was not my intent, and I apologize for that. I am grateful to Joseph P. Wilson who helped me understand how to avoid that impression. I can’t change the post that went out yesterday, but I will be more careful in future blog posts.


At the CS for All Consortium Celebration Tuesday, Brenda Wilkerson gave the closing keynote. The full livestream of the CS for All Summit is available here, and it includes Brenda’s talk. I’m a huge fan of Brenda, and she’s done fabulous work in Chicago. She is a leader in bringing CS to All.

I have not seen Brenda’s talk or any of the livestream. My experience of the Consortium Celebration was through reading the Twitter stream as I found time during the day. Brenda had one slide (which you can see in the tweet linked here) that I disagreed with, and because it’s an important point, I’m going to respond to it here.

It says, “Computer science builds the mental discipline for breaking down problems, and solving them.” There are few studies that test this claim as “computer science,” but there have been lots of studies looking for transfer from teaching programming to general problem-solving skills. Probably the first study investigating this claim is Roy Pea and Midian Kurland’s paper On the cognitive effects of learning computer programming. You can find this claim in a paper by Henry Walker to which I responded in this blog. You can see it in posts all over the Internet, from this blog post to this article from a teacher in England. There is a strong belief out there that learning computer science, and programming called out specifically, leads to new problem-solving and “a new way to think.”

There is simply not evidence in support of these claims. I talk about these in my book, I reference the Palumbo meta-review in this blog post, and NYTimes wrote about it this last spring. Like “learning styles” and “Latin teaches thinking,” this is a persistent myth that learning computing leads to problem-solving skills, and we have no support the claim.

I tweeted in response to Brenda’s slide, and several CS teachers asked me, “So why teach programming or computing at all?”  That’s a great question!  Here are some of my top reasons:

  1. To understand our world. The argument that Simon Peyton Jones made in England for their computer science curriculum is that Computer Science is a science like all the others. We teach Chemistry to students because they live in a world with chemical interactions. We teach Biology because they live in a world full of living things. We teach Physics because they live in a physical world. We should teach Computer Science because they live in a digital world.
  2. To study and understand processes. Alan Perlis (first ACM Turing Award laureate) argued in 1961 that everyone on every campus should learn to program. He said that computer science is the study of process, and many disciplines need people to know about process, from managers who work on logistics, to scientists who try to understand molecular or biological processes. Programming automates process, which creates opportunities to simulate, model, and test theories about processes at scale. Perlis was prescient in predicting computational science and engineering.
  3. To be able to ask questions about the influences on their lives. C.P. Snow also argued for everyone to learn computing in 1961, but with more foreboding. He correctly predicted that computers and computing algorithms were going to control important aspects of our lives. If we don’t know anything about computing, we don’t even know how to ask about those algorithms. It shouldn’t be magic.  Even if you’re not building these algorithms, simply knowing about them gives you power. C.P. Snow argues that you need that power.
  4. To use an important new form of literacy. Alan Kay made the argument in the 1970’s that computing is a whole new medium. In fact, it’s human’s first meta-medium — it can be all other media, and it includes interactivity so that the medium can respond to the reader/user/viewer. Computing gives us a new way to express ideas, to communicate to others, and to explore ideas.  Everyone should have access to this new medium.
  5. To have a new way to learn science and mathematics. Mathematics places a critical role in understanding our world, mostly in science. Our notation for mathematics has mostly been static equations. But code is different and gives us new insights. This is what Andy diSessa has been saying for many years. Bruce Sherin, Idit Harel, Yasmin Kafai, Uri Wilensky, and others have shown us how code gives us a powerful new way to learn science and mathematics. Bootstrap explicitly teaches mathematics with computing.  Everyone who learns mathematics should also learn computing, explicitly with programming.
  6. As a job skill. The most common argument for teaching computer science in the United States is as a job skill.  The original Code.org video argued that everyone should learn programming because we have a shortage of programmers. That’s just a terrible reason to make every school child learn to program. That’s what Larry Cuban was arguing this last summer. Tax payers should not be funding a Silicon Valley jobs program. Not everyone is going to become a software developer, and it doesn’t make any sense to train everyone for a job that only some will do. But, there’s some great evidence from Chris Scaffidi (that I learned about from Andy Ko’s terrific VL/HCC summary) showing that workers (not software developers) who program make higher wages than those comparable workers who do not. Learning to program gives students new skills that have value in the economy. It’s a social justice issue if we do not make this economic opportunity available to everyone.
  7. To use computers better. This one is a possibility, but we need research to support it. Everyone uses computers all the time these days. Does knowing how the computer works lead to more effective use of the computer?  Are you less likely to make mistakes? Are you more resilient in bouncing back from errors? Can you solve computing problems (those that happen in applications or with hardware, even without programming) more easily?  I bet the answer is yes, but I don’t know the research results that support that argument.
  8. As a medium in which to learn problem-solving. Finally, computer programming is an effective medium in which we can teach problem-solving. Just learning to program doesn’t teach problem-solving skills, but you can use programming if you want to teach problem-solving. Sharon Carver showed this many years ago. She wanted students to learn debugging skills, like being able to take a map and a set of instructions, then figure out where the instructions are wrong. She taught those debugging skills by having students debug Logo programs. Students successfully transferred those debugging skills to the map task. That’s super cool from a cognitive and learning sciences perspective. But her students didn’t learn much programming — she didn’t need much programming to teach that problem solving skill.But here’s the big caveat: They did not learn enough programming for any of the other reasons on this list!  The evidence we have says that you can teach problem-solving with programming, but students won’t gain more than that particular skill. That is a disservice to students.

Certainly there are more reasons than these, and I’ve seen several in the response to this blog post, and some in the comments below.

This was just one slide in Brenda’s talk. Her overall point was much more broader and more significant. I strongly agree with Brenda’s key point: CS for All is a social justice issue. Learning computing is so important that it is unjust to keep it from some students. Currently, CS is disproportionately unavailable to poorer students, to females, and to minority ethnic groups. We need CS for All.

October 18, 2017 at 12:30 pm 11 comments

Unpacking models of what the $USD1.3B might achieve in Computing Education: We need long-term vision and will

I wrote my Blog@CACM post for September on the massive investments in CS Education announced last week (see post here): $200M/year from the US Department of Education announced by the White House on Monday, then $300M over five years from the Tech industry announced on Tuesday. I have read analyses saying that the money isn’t really promised or isn’t new (see concerns in this post), and others are shunning the initiative because of White House policies (see link here). I took the promises at face value. My post starts congratulating Hadi Partovi and Cameron Wilson of Code.org and Ivanka Trump who were behind these initiatives, then I offered two back-of-the-envelope models of what $1.3B in five years could do:

  • I extrapolated the New York City model (of a significant computing education experience to every child in every school within grade bands) to the whole of the US, which would likely take more than a magnitude more funding.
  • The funding is enough to pay for a CS teacher in every school, but I argued that it wouldn’t really work. We face a shortage of STEM teachers, and those few are the teachers that we can most likely recruit to CS. CS teacher attrition is so high that we couldn’t keep up with the losses, since we have so few mechanisms of pre-service CS teacher preparation.

I received many responses, queries, and criticisms of that blog post (from email, Facebook, and Twitter).  I am explaining and unpacking the CACM blog post here. I am not going to delete or change the CACM blog post. My mentor, Janet Kolodner, told me once not to dwell on any paper, trying to make it a masterpiece before publishing it. Rather, she suggested that we should just keep publishing. Explore lots of ideas in lots of papers, and publish as a way of thinking with a community. It’s okay to publish something you thought was right, and later find that it’s wrong — it documents the explored trails.

What I learned about the effort in NYC

I said that NYC was aiming to provide a quality computing learning experience for every student in every grade in every school, as I learned last October (and blogged about it here). I learned that the goal is now mandating a computing learning experience in every grade band, so not every year. It’s still a markedly different model than one teacher per school, and doesn’t change the costs considerably.

I learned that (as one might expect) that the effort in NYC is in both the NYC Department of Education and in CSNYC. It’s great that there are many people in the NY DoEd working on CS education! I was told on Twitter that some of what I attributed to CSNYC is actually in NY Department of Education. I don’t know what I mis-attributed, but I’m sure that it’s because I get confounded over “CSNYC” representing “the effort to provide CS education across NYC” and “the organization that exists to provide CS education across NYC.” I don’t understand the split between NYC DoEd and the CSNYC organization, and I’m not going to guess here. I am sure that it’s important for the people involved, but it’s not so important for the model and national analysis.

Explaining my Estimates in Contrast to Code.org’s

Code.org has made their model of the one-time cost of expanding access to K-12 computer science (CS) available at this Google doc. According to their model, it’s clear that the $1.3B is enough to make CS education available in every elementary and secondary school. They have more empirical data than anyone else on putting CS in whole districts, and their data suggest that costs are decreasing as they gain more efficiencies of scale.

Hadi challenged several points in my blog post on Facebook. I won’t replicate all of our exchange, and only include three points here:

  • I argue that we will probably have to pay future CS teachers more in the future, at least as teacher stipends. That prediction is based on trends I see in the states I work with and economics. States are facing teacher shortages, especially in STEM. Aman Yadav shared an article (see link here) that students studying to be teachers fell by 40% from the 2010-2011 academic year to the 2014-2015 academic year. If the supply of teachers is growing more slowly than the rate at which we’re trying to grow CS, we will have to provide incentives to make CS more attractive. Lijun Ni’s dissertation explored the barriers for teachers to become CS teachers (e.g., it’s a lot easier and more pleasant to stay a math teacher). Costs are likely to grow as the labor shortage increases.
  • Some of my costs are too high, e.g., I estimated the cost to develop a high school CS teacher as $10K, where NSF’s studies found it was closer to $8.6K. I used a ballpark 50% of high school CS teacher development for the costs of elementary school CS teacher development.  Since it’s clear that there is enough to prepare one CS teacher per school, I think my numbers are close enough.
  • I believe that extrapolating the NYC model across the country would be even more expensive than it is in NYC. Travel costs in NYC are much less than in rural America. While NYC is very diverse, the rest of the United States is just as diverse. I got to see Ann Leftwich at Indiana University on Saturday. She told me that some of the schools she works with resist teaching science at all! It’s really hard to convince them to teach CS. I expect that there is a similar lack of will to teach CS across the US.

Not all of my estimates are research-based. We don’t have research on everything. Changing all US schools happens so rarely that we do not have good models of how it works. I don’t think that the empirical data of what we have done before in CS Ed is necessarily predictive of what comes next, since most of our experience with CS Ed at-scale is in urban and suburban settings. Getting everywhere is harder. I have observed about “Georgia Computes!” — 1/3 of the high schools in GA got someone that Barbara trained in CS, and that’s likely the easiest 1/3. The next 2/3 will be harder and more expensive.

What I Missed Entirely

As Hadi correctly called me on, the biggest cost factor I missed is the development of curriculum. Back in July, I blogged about Larry Cuban’s analysis that suggested that we need to re-think how we are developing and disseminating CS curriculum in the United States (see link here). We have to develop a lot more curriculum in collaboration with schools, districts, and states nationwide. The US will never adopt a single curriculum nationwide for any subject — it’s not how our system was developed, and it’s why Common Core did not reach all 50 states. The US education system is always about tailoring, adapting, and working with local values and politics. Curriculum is always political.

Mike Zamansky just posted a blog post critiquing some of the curriculum he’s seeing in NYC (see post here). I don’t agree with Mike’s post, but I wholeheartedly agree with his posting. We should argue about curriculum, negotiate what’s best for our students, and create curriculum that works for local contexts.  There is going to be a lot of that nationwide as we take steps towards providing computing education to all students. The iteration and revision will be expensive, but it’s a necessary expense for sustainable, longterm computing education.

What should we do with the money

At a talk I gave at Indiana University on Friday, Katie Siek asked me my opinion. What do I want to see the funding be used for?

It would be great if some of that funding could start more pre-service CS teacher preparation programs. I have argued that we should fund chairs of CS Education in top Schools of Education (see post here). Germany uses this model — they create CS Education professors who will be there for a career, producing CS teachers, supporting local communities of CS teachers, and serving as national models. An endowed chair is $1-3M at most universities. That is not very expensive for a longterm impact.

I prefer an NYC-like model of reaching every student to the model of a teacher for every school. The data I’ve seen from our ECEP states suggests that most CS teachers teach only a single computing class, and that class is typically mostly white/Asian and male. One CS  teacher per school doesn’t reach all the female and under-represented minority students. Equity has to be a top priority in our choices for these funds, since CS education is so inequitable.

My greatest wish is for computational literacy to be woven into other disciplines, especially across all of STEM. I devoted my career to computing education because I believe in the vision of Seymour Papert, Cynthia Solomon, Alan Kay, and Andrea diSessa. Computational literacy can improve learning in science, mathematics, art, language, and other disciplines, too.

I don’t argue that computer science is more important than other STEM subjects. Rather, computing makes learning in all the other STEM subjects better.

I want us to teach real computational literacy across subjects, not just in the CS class hidden away, and not just in an annual experience. I recognize that that’s a long-term, expensive vision — probably two orders of magnitude beyond the current initiative. We need more long-term thinking in CS education, like building up the CS teacher development infrastructure and making the case to people nationwide for CS education. We are not going to solve CS for All quickly.

When the K-12 CS Framework effort launched back in 2015, I told the story here about a conversation I had with Mike Lach (see post here). He pointed out that the last time we changed all US schools, it was in response to the Civil Rights movement. That’s when we started celebrating MLK Jr Day and added African-American History month. He asked me to think about how much national will it took to make those changes happen. We don’t have that kind of national will in CS education in this country — yet. We have a lot more groundwork to do before we can reach CS education for all students or all schools, and funding alone is not going to get us there.

October 4, 2017 at 7:00 am 6 comments

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

Universities need more Blacks: How do we know if we’re making progress?

The below article is pretty stunning — a sitting justice on the Supreme Court calling out an elite University for a lack of diversity.  This isn’t just about the University of Michigan. This isn’t about computing, but it could be. Sotomayor is speaking about an important social need, where computing is part of the problem.  We see that in the Generation CS report. We are falling further behind in getting African Americans into CS. (An interesting side note here that Georgia Tech alumna, Sarita Yardi (whom I mentioned in this blog post), just won an award at the University of Michigan for her work in promoting diversity.)

Daryl Chubin sent me a workshop report on “Better STEM Outcomes: Developing an Accountability System for Broadening Participation.” How would we know if we’re doing better?  We could measure participation rates in Universities, but that will take time to change.  How do you know if you’re doing the right things now for success later? For example, what would you measure at the high school level that would suggest progress towards broader participation in the future at the undergraduate level?  It’s a good question — we’re far from where we need to be, but we need to take meaningful steps towards the goal of broad participation in computing.

U.S. Supreme Court Justice Sonia Sotomayor on Monday said future diversity on college campuses is a key to diversifying society at large, noting the lack of black students at the University of Michigan is a “real problem.”

Sotomayor, the first Hispanic on the Supreme Court and daughter of Puerto Rican-born parents, was asked by a moderator what a university will need to look like in the years ahead to be inclusive and innovative.

“It’s going to look a lot like Michigan,” she said to applause, “but with even greater diversity.” The percentage of black undergraduate students at the University of Michigan has been pretty steady in recent years at less than 5 percent. Hispanics are 5.5 percent. White undergraduates are 65.4 percent.

Source: Sotomayor says University of Michigan needs more blacks

July 14, 2017 at 7:00 am 1 comment

From Design of Everyday Things to Teaching of Everyday People: Human error? Student error? No, Bad Design

We have to accept human behavior the way it is, not the way we would wish it to be.

Continue Reading June 26, 2017 at 7:00 am 19 comments

We need to separate Computing for All from Software Development: Claims that coding is not “fun,” it’s technically and ethically complex

The problem with the article linked below is that Code.org and the author mean two different things by the word “programming.”  Programming can be fun, insightful, sloppy, small, and still useful without demanding “superhuman focus” and “manic attention to detail.”  This is an issue I’ve talked about with respect to the thick line between programmer and user where most people will be in the future. I’m teaching an ethics course this summer — building software for others is technically and ethically complex, as the author states.  But building software as an end-user, as a hobbyist, as a scientist or engineer exploring an idea?  We need a different word.

Programming computers is a piece of cake. Or so the world’s digital-skills gurus would have us believe. From the non-profit Code.org’s promise that “Anybody can learn!” to Apple chief executive Tim Cook’s comment that writing code is “fun and interactive,” the art and science of making software is now as accessible as the alphabet.

Unfortunately, this rosy portrait bears no relation to reality. For starters, the profile of a programmer’s mind is pretty uncommon. As well as being highly analytical and creative, software developers need almost superhuman focus to manage the complexity of their tasks. Manic attention to detail is a must; slovenliness is verboten. Attaining this level of concentration requires a state of mind called being “in the flow,” a quasi-symbiotic relationship between human and machine that improves performance and motivation.

Source: Coding is not “fun,” it’s technically and ethically complex — Quartz

June 19, 2017 at 7:00 am 5 comments

Using tablets to broaden access to computing education: Elliot Soloway and truly making CS for All

I recently had the opportunity to visit with my PhD advisor, Elliot Soloway. Elliot has dramatically changed the direction of his research since we worked together. And he’s still very persuasive, because now I keep thinking about his challenge to push educational technology onto the least expensive devices.

When I worked with Elliot in the late 1980’s and early 1990’s, we emphasized having lots of screen real estate. Though the little Macintosh Plus was still popular through much of that time, Elliot was hooking up 21-inch, two page displays for all our development and at the high schools where we worked. The theoretical argument was the value of multiple-linked representations (like in this paper from Bob Kozma). By giving students multiple representations of their program and their design, we would facilitate learning across and between representations. The goal was to get students to see programming as design.

But in the mid 1990’s, Elliot changed his direction to emphasize inexpensive, handheld devices. I remember asking him why at the time, and he pointed out that you could give 10 students access to these low-cost devices for one of the higher-end devices. And access trumps screens.

Now, Elliot has a project, Intergalactic Mobile Learning Center, that produces software for learning that runs on amazingly inexpensive computers. Go to http://www.imlc.io/apps and try out their all-HTML software on any of your devices.

I purchased an Amazon Fire HD 8 tablet last year as a media consumption device (reading, videos, and music). For less than $100, it’s an amazingly useful device that I carry everywhere since it’s light and mostly plastic. Here’s some of IMLC’s software running on my inexpensive tablet.

Teaching Computer Science on a Tablet

I have been arguing in this blog that we need a greater diversity of teaching methods in computer science, to achieve greater diversity and to teach students (and reach students) who fail with our existing methods. Elliot’s argument for inexpensive tablets has me thinking about the value for computing education.

If our only CS teaching method is “write another program,” then a tablet makes no sense. Typing on a tablet is more difficult than on a laptop or desktop computer. I have been arguing that we can actually teach a lot about coding without asking students to program. If we expand our teaching methods to those that go beyond simply writing programs, then a tablet makes a lot of sense.

Could a focus on using tablets to teach computer science drive us to develop new methods? If more CS teachers tried to use tablets, might that lead to greater adoption of a diverse range of CS teaching methods?

Elliot’s argument is about bridging the economic and digital divide. Can we use the low cost of tablets to break down economic barriers to learning computer science? Computing education via tablets may be key to the vision of CS for All. We can outfit a whole classroom with tablets much more cheaply than buying even mid-range laptops for an elementary or middle school classroom.  There are people suggesting that if we buy kids iPads, we’ll improve learning (e.g., Los Angeles schools).  I’m making the inverse argument.  If we as computing curriculum/technology developers and teachers figure out how to teach computing well with tablets, we’ll improve learning for everyone.

I started checking out what I could do with my less than $100 tablet. I was amazed! Moore’s Law means that the low-end today is surprisingly capable.

GP, the new blocks-based programming language that I’ve been working with (see posts here and here), runs really well on my Fire HD 8 tablet. In fact, it runs better (more functionality, more reliable, greater stability) in the browser of my Fire tablet than the browser-based GP does on my iPad Pro (which costs about a magnitude more).  (There is an iOS version of GP which is fast and stable, but doesn’t have all the features of the browser-based version.)

GP running on a Fire HD 8 Tablet — two Media Computation projects (mirroring on left, removing red eye on right)

Our ebooks run well on the Fire HD 8 tablet. I can program Python in our ebook using the tablet. Our approach in the ebooks emphasizes modification to existing programs, not just coding from scratch. Tweaking text works fine on the tablet.

Running Python code on the Fire HD 8 Tablet

A wide range of CS education practice activities, from multiple choice questions to Parsons Problems, work well on the Fire HD 8.

Parsons Problem on Fire HD 8 Tablet

I tried out WeScheme on my Fire HD 8, too.

I bought the cheapest Chromebook I could find for this trip. I wanted a laptop alternative to take to China and for commuting on the Barcelona subway, rather than my heavier and more expensive MacBook Air. All of these browser-based tools (GP, Python programming in the ebook, Parsons Problems) run great on my $170 Acer Chromebook, plus I get a keyboard. Even a Chromebook would require different teaching and learning methods than what we use in many CS courses. I’m not going to run Eclipse or even JES on a Chromebook. (Though Emacs has been ported to the Chromebook, it only runs on certain Chromebooks and not mine). Google is aiming to merge Chromebook and Android development so that apps run on both. I don’t really understand all the differences between tablets and Chromebooks, but I do know that Chromebooks are becoming more common in schools.

A Chromebook costs about twice what a low-end tablet costs. While that is still much less than most laptops, twice is a big markup for a poor student or a budget-strapped school. It’s worth pushing for the lowest end.

CS education researchers, developers, and teachers should explore teaching computing with tablets. Some are doing this already. The next version of Scratch will run on mobile phones, and the current version will already run on some phones and tablets. Creating CS learning opportunities on low-end tablets will make computing education more affordable and thus accessible to a broader range of potential CS students.  My proposal isn’t about offering the poor a cheaper, low-quality alternative. Tablets force us to expand and diversify our teaching methods, which will lead us to create better and more accessible computing education for all.

June 14, 2017 at 7:00 am 9 comments

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