What’s unique about CS education compared to other DBERs?

I was recently asked by an NSF program officer to answer the questions, “What makes CS education different than other discipline-based education research (DBER, like math ed, physics ed, or engineering ed)? What research questions might we ask (that we might not currently be asking) to improve practice in CS education?” If I’m going to write a long-form email reply, I might as well make it a blog post. I’m using the specific term, computer science education, not my preferred and more general computing education because the question was framed specifically about CS programs, not necessarily about information technology, information systems, cybersecurity, or other computing programs.

Computer science education research has a quadruple-whammy right now that isn’t facing any other DBER that I know:

• ONE: We know less about how to do CS education well than we know about math, physics, science, or engineering education. A point I made in my SIGCSE keynote is that ASEE is 126 years old, NCTM started in 1920, and AAPT in 1950. CSTA started in 2004. With Ben duBoulay, I wrote the history chapter for the new Handbook of Computing Education Research. The field only dates back to 1967. Because CS is so new, there are few mechanisms to track progress at a systemic level. Most US states don’t gather data on CS like they do reading, science, mathematics, and other school subjects. We have less knowledge of how to teach and what’s going on because we’ve been at it for a shorter time.
• TWO: Below are two slides that I built but decided to edit out of my SIGCSE keynote talk. These are about the relative sizes of CS Ed and other DBER conferences. CS departments are desperately seeking more faculty (see the latest job ads analysis here). We have fewer practitioners and researchers than these other fields.

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• THREE: Perhaps a natural consequence of the first two: CS teachers know less of what we do know about evidence-based methods than STEM teachers in other fields. Charles Henderson showed that the vast majority of physics teachers in the US know about evidence-based teaching methods (over 80%) and try to use them (over 60%). Christopher Hovey presented evidence that it’s a small percentage for CS teachers (closer to 10%, see paper here). This might be expected given that we’re new (e.g., haven’t had the time to develop dissemination mechanisms that actually reach teachers) and there are relatively few teachers (compared to other disciplines) so it’s a smaller target to reach.
• FOUR: We are facing enormous economic demand for computer science. Undergraduate University CS enrollments are skyrocketing in the US. There’s a great story and infographic from UNC this last week on their enrollment demands.
  

The result is that we’re providing CS education to many students with few resources (teachers) and without a whole lot of data or use of evidence-based methods. From a research perspective, it’s also interesting that lots of students are resisting CS education — which is pretty common across STEM education. Students complain about algebra, calculus, physics, chemistry, and so on. The interesting twist is that students resisting CS ed are also then resisting the economic benefits, which makes it a bit more intriguing to study. The incentives are there, but many students still find the costs greater than the benefits.

Some of the research questions that I find interesting which are unique to CS education research:

• Why isn’t the enrollment boom extending to high schools?. Undergraduate education is exploding, but over 90% of US high school students are avoiding computer science, even when it’s being offered (which Miranda Parker explored in her dissertation). These are much lower numbers than in other STEM fields. (See blog post here about the low CS numbers in high schools, and this blog post comparing CS to other STEM fields.) We need ethnographic work and design work, to understand what’s going on and to document what might influence students to find CS more attractive.
• What would computing education look like for the other 90%? If we wanted to invent computing education that would reach the rest of US high school students, what would it look like? I suspect that the answer is going to be mostly about integrating CS into other-than-CS classes (like Bootstrap, STEM-CT, and Project GUTS). It’s an issue both of engaging students and getting teachers to adopt. I’m working on task-specific programming with teachers informing the design (see post here), to create programming that they’ll actually adopt and integrate into their non-CS classes. Katie Cunningham is working on inventing CS education that is focused on user needs rather than programming language demands. This is an area where we need a lot of design studies to explore a wide range of possibilities.
• What’s going on in community college CS? I know of studies of CS education at the high school level, the four year college, and the university level. I know of few studies at the community college level. How do they manage the economic imperative of CS education with preparing the students to go on to university? How do they motivate students to complete degrees if students just want to get a good job?
• What’s going on in undergraduate CS classes, especially during the enrollment boom? One of Lecia Barker’s greatest hits was her definition of the “defensive climate,” how students in CS are more about competing than collaborating, and even questions in class are more about showing-off than gaining knowledge. We published a paper that drew on defensive climate research at ITICSE last year (see blog post) — and that was one of the few papers published on the subject since Lecia’s ground-breaking work in 2002-2004. As I search in Google Scholar, I see a couple papers from Colleen Lewis (2011 and 2013), but other than those, there are very few papers testing or extending these notions over the last 15 years. Is “defensive climate” still an issue? I bet it is. How do we test for it? How do we address it? Is there a difference in climate between liberal arts and engineering based CS? How does the climate impact diversity? We have few studies of what’s going on in CS classes under these extreme conditions these days.
• How do we improve teaching quality in CS education? CS education has an issue like Engineering, but unlike science and math. I bet few calculus teachers are seriously swayed by, “How do professional mathematicians use calculus?” But in CS Ed, we’re always swayed by that economic benefit — many CS teachers worry about preparing students for current jobs, for current tools and languages. That focus on industry may inhibit a focus on pedagogy, but that’s a hypothesis to be explored. How do we teach CS teachers to know and use better teaching methods? What influences adoption of new teaching methods? This is particularly an important question in post-secondary where we have such extreme enrollment pressure. When I talk to CS teachers about new methods, the most common response is, “Sure, but when could I learn to do that?!?”
• What influences access to CS education?. When I teach my class on CS education research, I ask my students to identify open research questions. Last semester (see blog post here), a lot of their questions were about access to CS classes, which is complicated by the unique issues of CS education: How do parents’ education level/career influence student choices in CS, e.g. ,to take a CS class, to get a CS degree, to seek a CS job, etc.? Do students with learning disabilities (e.g., dyslexia) view code differently, and does that influence their participation in CS? Could we use fMRI or eye tracking to measure this? Why don’t more lower-income students go into CS, especially since it has such a large economic benefit? What percentage of current CS students are lower-income? How many lower-income students have the opportunity to learn CS and don’t take it?

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With this post, I’m taking a break from the blog, both for the holidays and to deal with some intense proposal writing. It’s been an exciting year. I’m going to end with a picture from the recent Georgia Tech PhD graduation ceremony. Not only did I get to hood Dr. Miranda Parker, but Barbara and I watched our son, Dr. Matthew Guzdial, get his doctoral hood. It’s a nice bright spot to close out the year. I wish you a happy holiday season and a successful 2020.

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.

Belief in the Geek Gene may be driven by Economics and Educational Inefficiency, plus using blocks to cross language boundaries

I visited China in the first part of May. I was at Peking University (PKU) in Beijing for a couple days, and then the ACM Celebration of the Turing Award in China (TURC) in Shanghai. I mentioned the trip in this earlier blog post. I wrote a blog post for CACM on a great panel at TURC. The panelists discussed the future of AI, and I asked about the implications for computing education. Are we moving to a future where we can’t explain to students the computing in their daily lives?

A highlight of my trip was spending a day with students and teachers at PKU. I taught a seminar with 30+ advanced undergraduates with Media Computation (essentially doing my TEDxGeorgiaTech talk live). It was great fun. I was surprised to learn that several of them had learned programming first in high school in Pascal. Pascal lives as a pedagogical programming language in China!

Perhaps the most striking part of my seminar with the undergraduates was how well the livecoding examples worked (e.g., I wrote and manipulated code as part of the talk).  All the PKU students knew Java, most knew C++, some knew Python — though I knew none of that when I was planning my talk. I wanted to use a tool that would cross programming language boundaries and be immediately understandable if you knew any programming languages. I used a blocks-based language.  I did my livecoding demonstration entirely in GP. I tested their knowledge, too, asking for predictions (as I do regularly, having read Eric Mazur’s work on predictions before demos) and explanations for those predictions.  They understood the code and what was going on. The funky sound and image effects cross language barriers.  Students laughed and oohed at the results.  Isn’t that remarkable that it worked, that I could give a livecoding demonstration in China and get evidence that the students understood it?

The most interesting session at PKU was talking with faculty interested in education about their classes and issues. I’ve always wondered what it’s like for students to learn programming when English is not their native language, and particularly, when the characters are very different. I asked, “Is it harder for your students to learn programming when the characters and words are all English?” The first faculty to speak up insisted that it really wasn’t an issue. “Our students start learning English at age 6!” said one. But then some of the other faculty spoke up, saying that it really was a problem, especially for younger students. In some middle schools, they are using Squeak with Chinese characters. They told me that there was at least one programming language designed to use Chinese characters, but the other faculty scoffed. “Yi is not a real programming language.” There was clearly some disagreement, and I didn’t follow all the nuances of the argument.

Then the Geek Gene came up in the conversation. One of the most senior faculty in the room talked about her challenges in teaching computer science. “Some students are just not suited to learning CS,” she told me. I countered with the evidence of researchers like Elizabeth Patitsas that there is no “Geek Gene.” I said, “We have no evidence that there are students who can’t learn programming.” She had an effective counter-argument.

“We do not have all the time in the world. We cannot learn everything in our lifetime. How much of a lifetime should a student spend learning programming? There are some students who cannot learn programming in the time available. It’s not worth it for them.”

I had not thought of the Geek Gene as being an economic issue. Her argument for the Geek Gene is not necessarily that students cannot learn programming. They may not be able to learn programming in the time available and using the methods we have available. This is not Geek Gene as only some students can learn to program. This is Geek Gene as economic limitation — we can’t teach everyone in the resources available.

I have an answer to that one. Want to reach more students? Either expand the time it will take to teach them, or use more effective methods!  This is the same response that I had offered to my colleague, as I described in an earlier blog post.

That insight gave me a whole new reason for doing our work in efficient CS education, like the greater efficiency in using subgoal-based instruction. The work of Paul Kirschner and Mike Lee & Amy Ko also emphasizes more CS learning in less time. If we can teach the same amount of CS in less time, then we can expand the number of students who can learn enough CS with a given amount of resource (typically, time). If we can’t convince teachers that there is no Geek Gene, maybe we can give them more effective and efficient teaching methods so that they see fewer students who don’t seem have the Geek Gene, i.e., who can learn enough CS in a single semester.

Below, evidence I was really at TURC

The role of higher education in reducing inequity: Using tuition, drop-out rates, and opportunity hoarding

This blog post isn’t about computing education.  You might want to simply delete this email, or skip over this post.  I’m using blog author’s prerogative to talk about things that fascinate me, even if they’re not in the title of the blog.

As frequent readers know, I increasingly read and think about economics, particularly with respect to higher education.  I’m going to collect in one blog post here (so that I don’t stray too far from the focus of the blog) some of the ideas and articles that have more interested me recently.

From Gladwell’s Revisionist History, we know that diverting tuition from the rich kids to the poor kids is common in schools that aim to bring in more lower-SES students and address issues of social inequity.  Unfortunately, this isn’t always possible. Here in Georgia, we’re forbidden by law to use tuition revenue to offer scholarships to less-advantaged children.  Puts us in a rough place when competing with schools that can.

Simply put, scholarship aid is not keeping pace with the rising price of college. While half of all families use a scholarship of some type to pay for college, much of that money is coming in the form of “discounts” off the tuition bill. Tuition discounts grew from $30 billion in 2007 to more than $50 billion in 2015, according to the College Board.While tuition discounts are marketed as scholarships in a student’s financial-aid package, they are not really scholarships. It’s not like a donor gave money to support a needy student with academic or musical talent. Rather, the scholarship money was diverted from another student’s tuition check. Last year, the average tuition discount for first-year students reached a staggering 47 percent — that’s nearly half off the published sticker price of tuition, up from about 40 percent just seven years ago.

Source: As College Tuitions Rise, Scholarships Fail to Keep Pace | Jeff Selingo | Pulse | LinkedIn

Without a doubt, one of saddest features of US higher education economics today: many of the kids saddled with higher education debt don’t even graduate! This is the awful perfect storm of increasing student debt and declining completion rates. Now, these students have massive debt, but don’t have the degree to get them a better paying job.

The author of the article linked below, Michael Crow, President of Arizona State University and author of Designing the New American University, visited at Georgia Tech this week, the day after Donald Trump became President-Elect of the US.  ASU has programs explicitly targeting those students, to help them get a degree that gives them entree to a better paying job that can help them to pay down their debt.  Crow said that the anger in this population is enormous — when they get saddled with debt, and higher ed fails them, they want to just blow up the system.  They’re through with how the existing system works.  Crow suggests that voices like that were what swept Trump to his surprising triumph.

Think about it: Tens of millions of people in the US are saddled with student debt and have no degree to help pay it off. They won’t get the substantial return on their investment—graduates with a bachelor’s degree earn about $1 million more in additional income over their lifetime than those with only a high school diploma—and they typically have not developed the adaptive learning skills that will help them prosper in a rapidly changing economy.In too many cases, they may never recover, leaving them feeling frustrated and bitter, disenfranchised and unable to find a way to better jobs and greater opportunity. Too many, saddled with debt and lacking a degree, feel trapped.

According to US Department of Education data, the ability to repay college loans depends more on whether a student graduated than on how much debt they are carrying. The research also found that students who don’t graduate are three times more likely to default on their loans than those who do.

Source: The Biggest Crisis in Higher Ed Isn’t Student Debt, It’s Students Who Don’t Graduate | Michael Crow | Pulse | LinkedIn

This last one is one that I saw linked to Emmanuel Schanzer’s wall in Facebook, and is deeply distressing. Rich kids who drop out of high school do as well as poor kids who complete college? Opportunity hoarding makes it difficult to really move the needle in terms of addressing economic inequity.  Crow talked about these kinds of inequalities in his talk, too.  If you’re in the bottom quartile in the US, you have an 8% chance of getting an undergraduate degree.  If you’re in the top quartile, you have an 80% chance — even if you do much worse in academics than the poor kids.

Even poor kids who do everything right don’t do much better than rich kids who do everything wrong. Advantages and disadvantages, in other words, tend to perpetuate themselves. You can see that in the above chart, based on a new paper from Richard Reeves and Isabel Sawhill, presented at the Federal Reserve Bank of Boston’s annual conference, which is underway.

Specifically, rich high school dropouts remain in the top about as much as poor college grads stay stuck in the bottom — 14 versus 16 percent, respectively. Not only that, but these low-income strivers are just as likely to end up in the bottom as these wealthy ne’er-do-wells. Some meritocracy.

What’s going on? Well, it’s all about glass floors and glass ceilings. Rich kids who can go work for the family business — and, in Canada at least, 70 percent of the sons of the top 1 percent do just that — or inherit the family estate don’t need a high school diploma to get ahead. It’s an extreme example of what economists call “opportunity hoarding.” That includes everything from legacy college admissions to unpaid internships that let affluent parents rig the game a little more in their children’s favor.

Source: Poor kids who do everything right don’t do better than rich kids who do everything wrong – The Washington Post

Higher Ed Might Help Reduce Inequity (mostly doesn’t): Gladwell’s Revisionist History podcast

Malcolm Gladwell’s new podcast, Revisionist History, recently included a mini-series about the inequities in society that higher education perpetuates. Higher education is a necessity for a middle class life in today’s US, but not everyone gets access to higher education, which means that the economic divide grows larger. We in higher education (an according to Richard Tapia in his foreword to Stuck in the Shallow End, we in computer science explicitly) may be playing a role in widening the economic divide. David Brooks wrote about these inequities in 2005, in his NYTimes column, titled “The Education Gap“:

We once had a society stratified by bloodlines, in which the Protestant Establishment was in one class, immigrants were in another and African-Americans were in another. Now we live in a society stratified by education. In many ways this system is more fair, but as the information economy matures, we are learning it comes with its own brutal barriers to opportunity and ascent.

Gladwell has written about higher education before. In David and Goliath: Underdogs, misfits, and the art of battling giants, he told the story of Caroline Sacks who loved science since she was a little girl. When she applied to college, she was accepted into both University of Maryland and Brown University. She chose Brown for its greater prestige. Unfortunately, that prestige came with a much more competitive peer set. Caroline compared herself to them, and found herself wanting. She dropped out of science. Gladwell suggests that, if she’d gone to Maryland, she might have persisted in science because she would have fared better in the relative comparison.

Gladwell’s three podcasts address who gets in to higher education, how we pay for financial aid for poorer students, and how we support institutions that serve poorer students.

In Carlos doesn’t remember, Gladwell considers whether there are poorer students who have the academic ability to succeed but aren’t applying to colleges. Ivy League schools are willing to offer an all-expenses-paid scholarship to qualified students whose family income is below a certain level, but they award few of those scholarships. The claim is that there are just few of those smart-enough-but-poor students. Economists Avery and Hoxby explored that question and found that there are more than 35,000 students in the United States who meet the Ivy League criteria (see paper here). So why aren’t they applying for those prestigious scholarships?

Gladwell presents a case study of Carlos, a bright student who gets picked up by a program aimed at helping students like him get access to high-quality academic opportunities. Gladwell highlights the range of issues that keep students like Carlos from finding, getting into, and attending higher education opportunities. He provides evidence that Avery and Hoxby dramatically underestimate the high-achieving poor student, e.g., Avery and Hoxby identified some students using eighth grade exam scores. Many of the high-achieving poor students drop out before eighth grade.

As an education researcher, I’m recommending this podcast to my graduate students. The podcast exemplifies why it’s so difficult to do interview-based research. The title of the episode comes from Carlos’s frequent memory lapses in the interview. When asked why he didn’t mention the time he and his sister were taken away from their mother and placed in foster care, Carlos says that he doesn’t remember that well. It’s hard to believe that a student this smart forgets something so momentous in his life. Part of this is a resilience strategy — Carlos has to get past the bad times in his life to persist. But part of it is a power relationship. Carlos is a smart, poor kid, and Gladwell is an author of international bestsellers. Carlos realizes that it’s in his best interest to make Gladwell happy with him, so he says what he thinks Gladwell wants to hear. Whenever there is a perceived power gap between an interviewee (like Carlos) and an interviewer (Gladwell), we should expect to hear not-quite-the-truth. The interviewee will try to tell the interviewer what he thinks the world-famous author wants to hear — not necessarily what the interviewee actually thinks.

The episode Food Fight contrasts Bowdoin College in Maine and Vassar College in New York. They are similar schools in terms of size and academics, but Bowdoin serves much better food in its cafeterias than Vassar. Vassar made an explicit decision to cut back in its food budget in order to afford more financial aid to its poorer students. Vassar spends almost twice as much as Bowdoin in financial aid, and has a much higher percentage of low-income students than Bowdoin. Vassar is explicit in the trade-offs that they’re making. Gladwell interviews a student who complains about the food quality, but says that she accepts it as the price for having a more diverse student body.

But there’s a tension here. Vassar can only afford that level of financial aid because there is a significant percentage of affluent students who are playing full fare — and those affluent students are exactly the ones for which both Bowdoin and Vassar compete. Vassar can’t balance their budget without those affluent students. They can’t keep providing for the poorer students unless they keep getting their share of the richer students. Here’s where Gladwell starts the theme he continues into the third episode, when he tells his audience, “Never give to Bowdoin!”

The third episode, My Little Hundred Million, starts from Hank Rowan giving $100 million to Glassboro State University in New Jersey. At the time, it was the largest philanthropic gift ever to a higher education institution. Since then there have been others, but all to elite schools. Rowan’s gift made a difference, saving a nearly-bankrupt university that serves students who would never be accepted at the elites. It made a difference in providing access and closing the “Education Gap,” in exactly the way that David Brooks was talking about in 2005. So why are such large gifts going instead to schools like Stanford and Harvard, who don’t play a role in closing that gap? And why do the rich keep giving to the elite institutions? Gladwell continues the refrain from the last episode. Stop giving to Harvard! Stop giving to Stanford!

The most amazing part of the third episode is an interview with Stanford President, John Hennessy. Gladwell prods him to defend why Stanford should get such large gifts. Hennessy talks about the inability of smaller, less elite schools to use the money well. Do they know how to do truly important things with these gifts? It’s as if Hennessy doesn’t understand that simply providing access to poor students is important and not happening. Hennessy is painted by Gladwell as blind to the inequities in the economy and to who gets access to higher education.

I highly recommend all of Revisionist History. In particular, I recommend this three-part mini-series for readers who care about the role that higher education can play in making our world better. Gladwell tells us that higher education has a critical role to play, in terms of accepting a more diverse range of students through our doors. We won’t do much to address the problems by only focusing on the “best and brightest.” As Richard Tapia writes in his foreword to Stuck in the Shallow End, that phrase describes much of what we get wrong in higher education.

“Over the years, I have developed an extreme dislike for the expression ‘the best and the brightest,’ so the authors’ discussion of it in the concluding chapter particularly resonated with me. I have seen extremely talented and creative underrepresented minority undergraduate students aggressively excluded from this distinction. While serving on a National Science review panel years back, I learned that to be included in this category you had to have been doing science by the age of ten. Of course, because of lack of opportunities, few underrepresented minorities qualified.”

Closing the Education Gap requires us to think differently about who we accept into higher education, who we most need to be teaching, and how we pay for it.

The Invented History of ‘The Factory Model of Education’: Personalized Instruction and Teaching Machines aren’t new

When I was a PhD student taking Education classes, my favorite two-semester sequence was on the history of education.  I realized that there wasn’t much new under the sun when it comes to thinking about education.  Ideas that are key to progressive education movements date back to Plato’s Republic: “No forced study abides in a soul…Therefore, you best of men, don’t use force in training the children in the studies, but rather play. In that way you can also better discern what each is naturally directed toward.”  Here we have learning through games (but not video games in 300BC) and personalized instruction — promoted over 2400 years ago.  I named my dissertation software system Emile after Rousseau’s book with the same name whose influence reached Montessori, Piaget, and Papert decades later.

Audrey Watters takes current education reformers to task in the article linked below.  Today’s reformers don’t realize the history of the education system, that many of the idea that they are promoting have been tried before. Our current education system was designed in part because those ideas have already failed.  In particular, the idea of building “teaching machines” as a response to “handicraft” education was suggested over 80 years ago.  Education problems are far harder to solve than today’s education entrepreneurs realize.

Many education reformers today denounce the “factory model of education” with an appeal to new machinery and new practices that will supposedly modernize the system. That argument is now and has been for a century the rationale for education technology. As Sidney Pressey, one of the inventors of the earliest “teaching machines” wrote in 1932 predicting “The Coming Industrial Revolution in Education,”

Education is the one major activity in this country which is still in a crude handicraft stage. But the economic depression may here work beneficially, in that it may force the consideration of efficiency and the need for laborsaving devices in education. Education is a large-scale industry; it should use quantity production methods. This does not mean, in any unfortunate sense, the mechanization of education. It does mean freeing the teacher from the drudgeries of her work so that she may do more real teaching, giving the pupil more adequate guidance in his learning. There may well be an “industrial revolution” in education. The ultimate results should be highly beneficial. Perhaps only by such means can universal education be made effective.

via The Invented History of ‘The Factory Model of Education’.

The reality is that technology never has and never will dramatically change education (as described in this great piece in The Chronicle).  It will always be a high-touch endeavor because of how humans learn.

Education is fundamentally a human activity and is defined by human attention, motivation, effort, and relationships.  We need teachers because we are motivated to make our greatest efforts for human beings with whom we have relationships and who hold our attention.

In the words of Richard Thaler, there are no Econs (see recommended piece in NYTimes).

Computing education that everyone needs but isn’t about learning programming

My colleague, Amy Bruckman, wrote a blog post about the challenges that nonprofits face when trying to develop and maintain software.  She concludes with an interesting argument for computing education that has nothing to do with learning programming that everyone needs.  I think it relates to my question: What is the productivity cost of not understanding computing? (See post here.)

This is not a new phenomenon. Cliff Lampe found the same thing in a study of three nonprofits. At the root of the problem is two shortcomings in education. So that more small businesses and nonprofits don’t keep making this mistake, we need education about the software development process as part of the standard high-school curriculum. There is no part of the working world that is not touched by software, and people need to know how it is created and maintained. Even if they have no intention of becoming a developer, they need to know how to be an informed software customer. Second, for the people at web design firms who keep taking advantage of customers, there seems to be a lack of adequate professional ethics education. I teach students in my Computers, Society, and Professionalism class that software engineers have a special ethical responsibility because the client may not understand the problem domain and is relying on the knowledge and honesty of the developer. More people need to get that message.

via Dear Nonprofits: Software Needs Upkeep (Why we need better education about software development and professional ethics) | The Next Bison: Social Computing and Culture.

“Disruptive Innovation” in Universities is not as important as Value

The below-linked article by Jill Lepore is remarkable for its careful dissection of Christensen’s theory of “disruptive innovation.” (Thanks to Shriram Krishnamurthi for the link.)  As Lepore points out, Christensen’s theories were referenced often by those promoting MOOCs.  I know I was told many times (vehemently, ferociously) that my emphasis on learning, retention, diversity was old-fashioned, and that disrupting the university was important for its own sake, for the sake of innovation.  As Lepore says in the quote below, there may be good arguments for MOOCs, but Christensen’s argument from a historical perspective just doesn’t work.  (Ian Bogost shared this other critical analysis of Christensen’s theory.)

I just finished reading Michael Lewis’s The Big Short, and I see similarities between how Lepore describes reactions to Christensen’s theory of “disruptive innovation” and how Lewis describes the market around synthetic subprime mortgage bond-backed financial instruments.  There’s a lot of groupthink going on (and the Wikipedia description is worth reading), with the party line saying, “This is all so great!  This is a great way to get rich!  We can’t imagine being wrong!”  What Lewis points out (most often through the words of Dr. Michael Burry) is that markets work when there is a logic to them and real value underneath.  Building financial instruments on top of loans that would never be repaid is ludicrous — it’s literally value-less.  Lepore is saying something similar — innovation for its own sake is not necessarily valuable or a path to success, and companies that don’t disruptively innovate can still be valuable and successful.

I don’t know enough to critique either Lewis or Lepore, but I do see how the lesson of value over groupthink applies to higher-education.  Moving education onto MOOCs just to be disruptive isn’t valuable.  We can choose what value proposition for education we want to promote.  If we’re choosing that we want to value reaching students who don’t normally get access higher education, that’s a reasonable goal — but if we’re not reaching that goal via MOOCs (as all the evidence suggests), then MOOCs offer no value.  If we’re choosing that we want students to learn more, or to improve retention, or to get networking opportunities with fellow students (future leaders), or to provide remedial help to students without good preparation, those are all good value propositions, but MOOCs help with none of them.

Both Lewis and Lepore are telling us that Universities will only succeed if they are providing value. MOOCs can only disrupt them if they can provide that value better.  No matter what the groupthink says, we should promote those models for higher-education that we can argue (logically and with evidence) support our value proposition.

In “The Innovative University,” written with Henry J. Eyring, who used to work at the Monitor Group, a consulting firm co-founded by Michael Porter, Christensen subjected Harvard, a college founded by seventeenth-century theocrats, to his case-study analysis. “Studying the university’s history,” Christensen and Eyring wrote, “will allow us to move beyond the forlorn language of crisis to hopeful and practical strategies for success.” … That doesn’t mean good arguments can’t be made for online education. But there’s nothing factually persuasive in this account of its historical urgency and even inevitability, which relies on a method well outside anything resembling plausible historical analysis.

via Jill Lepore: What the Theory of “Disruptive Innovation” Gets Wrong : The New Yorker.

Let’s do the math: Does it make sense to fill a pipeline of CS workers from 3rd grade?

According to the article linked below, there is a large effort to fill STEM worker jobs in Northern Virginia by getting kids interested in STEM (including computing) from 3rd grade on.  The evidence for this need is that there will be 50K new jobs in the region between 2013 and 2018.

The third graders are 8 years old.  If they can be effective STEM workers right out of high school, there’s another 10 years to wait before they can enter the workforce — 2024.  If they need undergrad, 2028.  If they need advanced degrees, early 2030’s.  Is it even possible to predict workforce needs out over a decade?

Now, let’s consider the cost of keeping that pipeline going, just in terms of CS.  Even in Northern Virginia, only about 12% of high schools offer CS today.  So, we need a fourfold increase in CS teachers — but that’s just high school.  The article says that we want these kids supported in CS from 3rd grade on.  Most middle schools have no CS teachers.  Few elementary schools do.  We’re going to have to hire and train a LOT of teachers to fulfill that promise.

Making a jobs argument for teaching 3rd graders CS doesn’t make sense.

The demand is only projected to grow greater. The Washington area is poised to add 50,000 net new STEM jobs between 2013 and 2018, according to projections by Stephen S. Fuller, the director of the Center for Regional Analysis at George Mason University. And Fuller said that STEM jobs are crucial in that they typically pay about twice as much as the average job in the Washington area and they generate significantly more economic value.

It is against this backdrop that SySTEMic Solutions is working to build a pipeline of STEM workers for the state of Virginia, starting with elementary school children and working to keep them consistently interested in the subject matter until they finish school and enter the workforce.

via To create a pipeline of STEM workers in Virginia, program starts with littlest learners – The Washington Post.

Eisenhower’s Cross of Iron Speech: Trade-offs for the Defense Budget

I recently watched the documentary Why we fight, and was struck by the prescience of President Eisenhower’s warning.  So many of our educational decisions are made because of the harsh economic realities of today.  How many of these are guns-for-butter choices might we have made differently if education was considered?  Here in Georgia, computer science curricular decisions are being made with a recognition that there will be little or no funding available for teacher professional development — certainly not enough for every high school CS teacher in the state.  What percentage of the DoD budget would it cost to provide professional learning opportunities to every CS teacher in the country?  It’s certainly in the single digits.

Every gun that is made, every warship launched, every rocket fired signifies, in the final sense, a theft from those who hunger and are not fed, those who are cold and are not clothed.

This world in arms in not spending money alone.

It is spending the sweat of its laborers, the genius of its scientists, the hopes of its children.

The cost of one modern heavy bomber is this: a modern brick school in more than 30 cities.

It is two electric power plants, each serving a town of 60,000 population.

It is two fine, fully equipped hospitals.

It is some 50 miles of concrete highway.

We pay for a single fighter with a half million bushels of wheat.

We pay for a single destroyer with new homes that could have housed more than 8,000 people.

This, I repeat, is the best way of life to be found on the road the world has been taking.

This is not a way of life at all, in any true sense. Under the cloud of threatening war, it is humanity hanging from a cross of iron.

via Cross of Iron Speech.

Who Needs to Know How to Code? More than just the rich white boys

Wall Street Journal just ran an article (linked below) about people “flocking to coding classes.”  The lead for the story (quoted below) is a common story, but concerning.  If coding is all extra-curricular, with the (presumably expensive) once-a-week tutor, then how do the average kids get access?  How do the middle and lower kids get access?  Hadi Partovi and Jane Margolis talked about this on PRI’s Science Friday — CS education can’t be an afterschool activity, or we’ll keep making CS a privileged activity for white boys.

Like many 10-year-olds, Nick Wald takes private lessons. His once-a-week tutor isn’t helping him with piano scales or Spanish conjugations, but teaching him how to code.

Nick, a fifth-grader in New York, went in with no experience and has since learned enough HTML, JavaScript and CSS to build a simple website. He is now working in Apple’s XCode environment to finish an app named “Clockie” that can be used to set alarms and reminders. He plans to offer it in the iOS App Store for free.

“I always liked to get apps from the app store, and I always wanted to figure out how they worked and how I could develop it like that,” Nick says.

As the ability to code, or use programming languages to build sites and apps, becomes more in demand, technical skills are no longer just for IT professionals. Children as young as 7 can take online classes in Scratch programming, while 20-somethings are filling up coding boot camps that promise to make them marketable in the tech sector. Businesses such as American Express Co. AXP -0.57% send senior executives to programs about data and computational design not so they can build websites, but so they can better manage the employees who do.

via Who Needs to Know How to Code – WSJ.com.

SIGCSE Preview: Measuring Demographics and Performance in Computer Science Education at a Nationwide Scale Using AP CS Data

Barbara and I are speaking Thursday 3:45-5 (with Neil Brown on his Blackbox work) in Hanover DE on our AP CS analysis paper (also previewed at a GVU Brown Bag). The full paper is available here: http://bit.ly/SIGCSE14-APCS  This is a different story than the AP CS 2013 analysis that Barbara has been getting such press for.  This is a bit deeper analysis on the 2006-2012 results.

Here are a couple of the figures that I think are interesting.  What’s fitting into these histograms are states, and it’s the same number of bins in each histogram, so that one can compare across.

Fitting this story into the six page SIGCSE format was really tough.  I wanted to make the figures bigger, and I wanted to tell more stories about the regressions we explored.  I focused on the path from state wealth to exam-takers because I hadn’t seen that story in CS Ed previously (though everyone would predict that it was there), but there’s a lot more to tell about these data.

Figure 1: Histograms describing (a) the number of schools passing the audit over the population (measured in 10K), (b) number of exam-takers over the population, and (c) percentage of exam-takers who passed. 

Figure 2: Histograms describing (d) the percent of female exam-takers, (e) the number of Black exam-takers, and (f) the number of Hispanic exam-takers. 

Measuring Demographics and Performance in Computer Science Education at a Nationwide Scale Using AP CS Data

Abstract: Before we can reform or improve computing education, we need to know the current state. Data on computing education are difficult to come by, since it’s not tracked in US public education systems. Most of our data are survey-based or interview-based, or are limited to a region. By using a large and nationwide quantitative data source, we can gain new insights into who is participating in computing education, where the greatest need is, and what factors explain variance between states. We used data from the Advanced Placement Computer Science A (AP CS A) exam to get a detailed view of demographics of who is taking the exam across the United States and in each state, and how they are performing on the exam. We use economic and census data to develop a more detailed view of one slice (at the end of secondary school and before university) of computer science education nationwide. We find that minority group involvement is low in AP CS A, but the variance between states in terms of exam-takers is driven by minority group involvement. We find that wealth in a state has a significant impact on exam-taking.

 

Solving the technology access problem for on-line learning

We’ve heard about this problem before: Online courses don’t reach the low-income students who most need them, because they don’t have access to the technology on-ramp.  This was an issue in the San Jose State experiment.

That’s because the technology required for online courses isn’t always easily accessible or affordable for these students. Although the course may be cheaper than classroom-based courses, the Campaign for the Future of Higher Education argues in a report released Wednesday low-income students might still have a harder time accessing it.

“We have to wrap our heads around the fact that we can’t make assumptions that this will be so simple because everyone will just fire up their computers and do the work,” says Lillian Taiz, a professor at California State University, Los Angeles, and president of the California Faculty Association.

Many students, Taiz says, don’t have computers at home, high-speed Internet access, smart phones, or other technologies necessary to access course content.

via Online Programs Don’t Always Expand Access to Higher Education, Report Says – US News and World Report.

The US News article suggests Google Chromebooks as an answer — cheap and effective. The Indian government is trying an even cheaper tablet solution. Could you use one of these to access MOOCs?

The Indian government realized a few years ago that the technology industry had no motivation to cater to the needs of the poor. With low cost devices, the volume of shipments would surely increase, but margins would erode to the point that it wasn’t worthwhile for the big players. So, India decided to design its own low-cost computer.  In July 2010, the government unveiled the prototype of a $35 handheld touch-screen tablet and offered to buy 100,000 units from any vendor that would manufacture them at this price. It promised to have these to market within a year and then purchase millions more for students.

via The $40 Indian tablet that could help bridge America’s digital divide.

Poverty Impedes Cognitive Function

An interesting experiment, with a deeply disturbing result.

The poor often behave in less capable ways, which can further perpetuate poverty. We hypothesize that poverty directly impedes cognitive function and present two studies that test this hypothesis. First, we experimentally induced thoughts about finances and found that this reduces cognitive performance among poor but not in well-off participants. Second, we examined the cognitive function of farmers over the planting cycle. We found that the same farmer shows diminished cognitive performance before harvest, when poor, as compared with after harvest, when rich. This cannot be explained by differences in time available, nutrition, or work effort. Nor can it be explained with stress: Although farmers do show more stress before harvest, that does not account for diminished cognitive performance. Instead, it appears that poverty itself reduces cognitive capacity. We suggest that this is because poverty-related concerns consume mental resources, leaving less for other tasks. These data provide a previously unexamined perspective and help explain a spectrum of behaviors among the poor. We discuss some implications for poverty policy.

via Poverty Impedes Cognitive Function.

We measure educational productivity wrong: Not numbers-served but learning

The Washington Post series on “The Tuition is Too Damn High” has been fascinating, filled with interesting data, useful insights, and economic theory that I hadn’t met previously.  The article linked below is about “Baumol’s cost disease” which suggests an explanation for why wages might increase when productivity does not.  It’s an explanation that some have used to explain the rise in tuition, which Post blogger Dylan Matthews rejects based on the data (in short: faculty salaries aren’t really rising — the increase in tuition is due to other factors).

But I actually had a concern about an earlier stage in his argument.  It’s absolutely true that our labor intensive methods do not lead to an increase in productivity in terms of number of students, while MOOCs and similar other methods can.  However, we can gain productivity in terms of quality of learning and retention.  We absolutely have teaching methods, well-supported with research, that lead to better learning and more retention — we can get students to complete more classes with better understanding.  In the end, isn’t THAT what we should be measuring as productivity of an educational enterprise, not “millions of customers served” (even if they don’t complete and don’t learn)?

Performing a string quartet will always require two violinists, a violist and a cellist. You can’t magically produce the same piece with just two people. Higher education, for at least the past few millennia, has seemed to fall in this category as well. “What just happened in my classroom is not very different from what happened in Plato’s academy,” quips Archibald. We’ve gotten better at auditorium-building, perhaps, but lecturers generally haven’t gotten more productive.

via The Tuition is Too Damn High, Part V — Is the economy forcing colleges to spend more?.