NPR When Women Stopped Coding in 1980’s: As we repeat the same mistakes

The NPR Planet Money segment cited below is excellent.  I’m really glad that they reached out to Jane Margolis and Telle Whitney to get the history right.

The question that they don’t address in the segment is, “Why did the classes get so much harder in the mid-1980’s that only the boys who were playing with PCs could succeed at them?”

In the early 1980’s, interest in Computer Science spiked.  There was more interest than there were seats available in CS classes.  Eric Roberts talked about these times in his keynote at the Future of Computing Education Research workshop in January 2014, which I blogged about here.  What to do with the burgeoning enrollment and no additional resources? Caps were put into place, and classes became harder.  Berkeley raised their cap until you had to have 4.0 in all your pre-requisite CS classes to get accepted to the major.  Eric Roberts was chair of the CS department at Wellesley in the early 1980’s, and he told me about introductory CS classes at MIT with insane workload, where only the boys with lots of prior CS experience and who were fanatical about computing were getting through.  Jane Margolis and Alan Fisher talked about this phenomenon in Unlocking the Clubhouse when they describe how the men and women in the CS classes at CMU had different views of the computer, which influenced how they interacted with it and how much time they were willing to put into their classes (nice summary of this story is on Wikipedia).

The classes may not have been made harder explicitly to deal with overcrowding, i.e., to “weed out.” It may have happened in response to an influx of boys who already knew a lot from playing with their PC toys, compounded with a lack of resources because of the overcrowding. With boys who already knew a lot, CS teachers could start skipping over topics, or covering them lightly, or just assigning programming tasks so that the student “figures it out” on his or her own. If a student can’t learn with this approach, then teacher might decide that the student just “can’t” learn to program. Maybe the student doesn’t have the Geek Gene. Some students do succeed with this approach, because they know a lot from prior experience (or have the Geek Gene).

Now, put this in the setting of high enrollments and tight budgets.  A student with lots of prior experience needs less teacher time to succeed. A student with less experience needs more time and effort in order to succeed in CS classes. In lean times, there are fewer resources for teaching, and those with less experience will not get the resources they need to succeed. Students with more experience will succeed just fine, so we continue to have high-quality CS graduates who get good jobs. Unless we look carefully at who is succeeding and who isn’t, we might not even notice that our program now presumes prior experience in order for the student to succeed.

What’s scary is that we may now be following the exact same path.  Eric has been warning about this for some time (see blog post). Enrollment in CS is exploding nationwide.  Now, the caps are starting to be put into place.  Berkeley now requires a 3.0 in the pre-requisite classes to get in to the major.  Here at Georgia Tech, the College of Computing has just requested to have a grade requirement in pre-requisite CS classes before allowing students to transfer into CS.

It’s still the case that it’s mostly wealthier (middle or upper class), white or Asian males who get access to high school CS. That’s in Barb’s AP analysis that got so much coverage this last year (see blog post here and the media coverage here).  AP CS is the most gender-skewed AP (more male than AP Studio Art is female).  So, even if you’re in a school that can afford AP, women will most likely not be in the CS class. In our AP analysis SIGCSE paper last year, we showed how wealth in a state has a strong relationship with AP CS offerings in the state.  We’re now starting to show the relationship continues to the district level as appeared in this blog a few weeks ago.

These kinds of caps have two effects which limit access by women and under-represented minorities (the second of which was pointed out to me by Eric):

• First, the students who succeed the most in intro CS are the ones with prior experience.
• Second, creating these kinds of caps creates a perception of CS as a highly competitive field, which is a deterrent to many students.  Those students may not even try to get into CS.

I understand why caps are going into place.  We can’t support all these students, and there are no additional resources coming.  What else can CS departments do?We might think about a lottery or using something beyond CS GPA to get those seats, something that’s more equitable. State budgets for universities have been cut back across the US, and it’s not clear that anyone (companies or the Federal government) could swoop in and cover that shortfall.  In lean budget times, few university administrators (public or private) are willing to invest in CS right now.  There will likely be a push for more MOOCs in the introductory courses — which is exactly where MOOCs are least effective (see my article in Ubiquity.)

It looks likely that we are going to reduce the diversity in CS, again.  While on our watch.

Mark Zuckerberg. Bill Gates. Steve Jobs. Most of the big names in technology are men.But a lot of computing pioneers, the ones who programmed the first digital computers, were women. And for decades, the number of women in computer science was growing.But in 1984, something changed. The number of women in computer science flattened, and then plunged.

via Episode 576: When Women Stopped Coding : Planet Money : NPR.

Many in the ideas for this blog post came from discussions with the Diversity Task Force of the ACM Education Council. All the mistakes are mine.

Going beyond the cognitivist in computing education research questions

In Josh Tenenberg’s lead article in the September 2014 ACM Transactions on Computing Education (linked below), he uses this blog, and in particular, this blog post on research questions, as a foil for exploring what questions we ask in computing education research.  I was both delighted (“How wonderful! I have readers who are thinking about what I’m writing!”) and aghast (“But wait!  It’s just a blog post!  I didn’t carefully craft the language the way I might a serious paper!”) — but much more the former.  Josh is kind in his consideration, and raises interesting issues about our perspectives in our research questions.

I disagree with one part of his analysis, though.  He argues that my conception of computing education (“the study of how people come to understand computing”) is inherently cognitivist (centered in the brain, ignoring the social context) because of the word “understand.”  Maybe.  If understanding is centered in cognition, yes, I agree.  If understanding is demonstrated through purposeful action in the world (i.e., you understand computing if you can do with computing what you want), then it’s a more situated definition.  If understanding is a dialogue with others (i.e., you understand computing if you can communicate about computing with others), then it’s more of a sociocognitive definition.

The questions he calls out are clearly cognitivist.  I’m guilty as charged — my first PhD advisor was a cognitive scientist, and I “grew up” as the learning science community was being born.  That is my default position when it comes to thinking about learning.  But I think that my definition of the field is more encompassing, and in my own work, I tend toward thinking more about motivation and about communities of practice.

Asking significant research questions is a crucial aspect of building a research foundation in computer science CS education. In this article, I argue that the questions that we ask are shaped by internalized theoretical presuppositions about how the social and behavioral worlds operate. And although such presuppositions are essential in making the world sensible, at the same time they preclude carrying out many research studies that may further our collective research enterprise. I build this argument by first considering a few proposed research questions typical of much of the existing research in CS education, making visible the cognitivist assumptions that these questions presuppose. I then provide a different set of assumptions based on sociocultural theories of cognition and enumerate some of the different research questions to which these presuppositions give rise. My point is not to debate the merits of the contrasting theories but to demonstrate how theories about how minds and sociality operate are imminent in the very questions that researchers ask. Finally, I argue that by appropriating existing theory from the social, behavioral, and learning sciences, and making such theories explicit in carrying out and reporting their research, CS education researchers will advance the field.

via Asking Research Questions.

Adjunct Faculty are Unionizing

I wonder if this is the start of a trend that will change higher education.  The job of being faculty is becoming harder, especially in CS as enrollments rise without a rise in faculty numbers. Adjunct faculty are particularly put upon in universities, and unionizing is one way for them to push back.

Part-time faculty members at downtown Pittsburgh’s Point Park University have voted to join the Adjunct Faculty Association of the United Steelworkers AFA-USW.The group filed a petition with the National Labor Relations Board NLRB in April to hold a mail ballot election. A total of 314 part-time Point Park instructors were eligible to vote, and the ballots were counted this morning at the NLRB’s downtown offices.

via Point Park Adjunct Faculty Votes to Join AFA-USW Union | United Steelworkers.

Computational media women are retained to graduation

After my post claiming that Georgia Tech’s Computational Media program is the most gender-balanced, ABET-accredited computing undergraduate degree program in the United States, I had several people ask, “But that’s enrollment. Do the women graduate? Do they stick with the program?” My sense was that they did, but I asked our College data person, Elijah Cameron, and he sent me the below. Last year, BS in Computational Media graduates were over 40% female. Pretty good.

In Silicon Valley diversity conversations, age is left out

I’ve heard this from former students in Silicon Valley.  It’s hard to stay in the game for long, because you “age out.”

But one set of statistics has been noticeably absent: the age of those companies workers.Silicon Valleys conversation about diversity has revolved chiefly around gender and race, although the stereotype of the techie as white, male and young has written out the over-40 set as well.”Walk into any hot tech company and you’ll find disproportionate representation of young Caucasian and Asian males,” said Ed Lazowska, who holds the Bill & Melinda Gates chair in Computer Science & Engineering at the University of Washington. “All forms of diversity are important, for the same reasons: workforce demand, equality of opportunity and quality of end product.”

via In Silicon Valley diversity conversations, age is left out – SFGate.

The Growing Tide of Anti-Intellectualism

The issues raised about education are particularly relevant to this blog. State cutbacks of funding to universities send a message about what’s valued and what’s not. CS departments in state schools (and elsewhere) are facing enormous increases in enrollment, and without additional resources, are going to be imposing caps — which will serve to reduce the diversity of computing, as it did in the 1980’s. Where we place our resources indicates our values.

Spaf's Thoughts

There is an undeniable, politically-supported growth of denial — and even hatred — of learning, facts, and the educated. Greed (and, most likely, fear of minorities) feeds demagoguery. Demagoguery can lead to harmful policies and thereafter to mob actions.

I’ve written on this topic here before. I also have cited an excellent essay from Scientific American about how the rising tide of anti-intellectualism threatens our democracy and future (you should read it).

What prompts this post is a recent article about a thinly-veiled political probe of the National Science Foundation, combined with the pending national election in the US. (Some of these issues apply elsewhere in the world, but this is a US-centric post.)

This view is also reinforced by my current experience — I am on a combined speaking tour and family vacation in Poland. I recently visited a memorial to the Katyn massacre, remembering when Soviet…

View original post 1,026 more words

Women computer science grads: Raw numbers went up as percentages went down

Fascinating analysis! It turns out that the number of women getting degrees in CS actually rose in the early 2000’s, but the percentage shared dropped because so many men women were taking CS, too.

Here’s the number of women getting CS degrees:

Here’s the percentage view:

The gains by women actually weren’t keeping up with the overall increase in the population of CS grads. More men were filling those seats than women. As a share of all CS bachelor’s degrees granted that year, females had slipped almost 10 points, from 37% in 1984/1985 to 27% in 2003. The overall trendline was clearly downward, as seen below.

via Women computer science grads: The bump before the decline | Computerworld.

Learners don’t know what’s best for them

Annie Murphy Paul has a nice article about autodidacts — yes, there are some, but most of us aren’t.  MOOCs are mostly for autodidacts.  The paper from Educational Psychologist is excellent, and I reading the original as well as Paul’s review.

In a paper published in Educational Psychologist last year, Jeroen J.G. van Merriënboer of Maastricht University and Paul A. Kirschner of the Open University of the Netherlands challenge the popular assumption “that it is the learner who knows best and that she or he should be the controlling force in her or his learning.”

There are three problems with this premise, van Merriënboer and Kirschner write. The first is that novices, by definition, don’t yet know much about the subject they’re learning, and so are ill equipped to make effective choices about what and how to learn next. The second problem is that learners “often choose what they prefer, but what they prefer is not always what is best for them;” that is, they practice tasks that they enjoy or are already proficient at, instead of tackling the more difficult tasks that would actually enhance their expertise. And third, although learners like having some options, unlimited choices quickly become frustrating—as well as mentally taxing, constraining the very learning such freedom was supposed to liberate.

via Ed tech promoters need to understand how most of us learn | The Hechinger Report.

Why the ‘coding for all’ movement is more than a boutique reform – Margolis and Kafai respond to Cuban in Washington Post

Highly recommended reading — Jane Margolis and Yasmin Kafai respond to the concerns of Larry Cuban about the “coding for all” movement (that I blogged on here).  They address a wide range of issues, from the challenges of changing school to the importance of education about coding for empowerment.

On a functional level, a basic understanding of code allows for an understanding of the design and functionalities that underlie all aspects of interfaces, technologies, and systems we encounter daily. On a political level, understanding code empowers and provides everyone with resources to examine and question the design decisions that populate their screens. Finally, on a personal level, everyone needs and uses code in some ways for expressive purposes to better communicate, interact with others, and build relationships. We need to be able to constructively, creatively, and critically examine designs and decisions that went into making them.

via Why the ‘coding for all’ movement is more than a boutique reform – The Washington Post.

Is There a Crisis in Computer-Science Education? Decrease in graduation rates in CS

We’ve talked about this problem before — that it looks like we’re graduating fewer CS undergraduates, despite rising enrollment.  Interesting analysis in The Chronicle:

Aside from looking remarkably like the Cisco logo itself a representation of San Francisco’s iconic Golden Gate Bridge, the chart clearly shows fluctuation in interest among undergraduates and graduates in computer science.The reason for that fluctuation isn’t clear from the graph, but we have a couple of theories:

1. The pipeline was primed: In the 1970s and 1980s, many elementary, middle, and high schools taught computer programming to students, according to Joanna Goode. As an associate professor of education studies at the University of Oregon, Ms. Goode has researched access for women and students of color in computer science.“But, as the PC revolution took place, the introduction to the CD-ROMS and other prepackaged software, and then the Internet, changed the typical school curriculum from a programming approach to a ‘computer literacy’ skill-building course about ‘how to use the computer,’”…
2. The job market: Fluctuations in college-degree attainment are often connected to fluctuations in the job market in certain industries.

via Is There a Crisis in Computer-Science Education? – Data Points – Blogs – The Chronicle of Higher Education.

Teaching Computer Science Better to get Better Results

This is my third blog post in a series inspired by a thread in the SIGCSE-Members list and by the Slate article which argued that “Practice doesn’t make perfect.” Macnamara et al did a meta-analysis of studies of expertise, and found that a relatively small percentage of variance in expertise can be explained through hours of practice. The Slate authors argue that this implies that genetics explains the rest of the variance.

• In the first post (see here), I argued that the practice+genetics is too simple to explain expertise. First, practice can be deliberate, lazy, or teacher-led. Second, there is experience that leads to expertise which is between genetics and practice. The most significant flaw of both Macnamara et al. and Ericsson et al. is ignoring teaching.
• In the second post (appearing yesterday in Blog@CACM), I addressed a claim in the SIGCSE-Members list that programmers are “wired” differently than others. Most CS teachers agree with the Slate authors, that students can NOT be more successful with more work. The evidence that better teaching leads to better learning is overwhelming. In fact, there is significant evidence that teaching can even overcome genetic/innate-ability differences.

Lots of CS teachers believe in the Geek Gene Hypothesis, and for good reason. It’s frustrating to have seemingly no impact on some, especially the lower-end, students. Even the award-winning Porter, Zingaro, and Lister paper points out that the earliest assessments in the class they studied correlate very highly with the final grade. Gas Station without Pumps voiced a similar sentiment in his blog post in response to the Slate article:

But the outcomes for individual students seem to depend more on the students coming in than on what I do.  Those students who come in better prepared or “innately” smarter progress faster than those who come in behind, so the end result of the teaching is that differences among the students are amplified, not reduced. Whether the differences in the students coming in are due to prior practice, prior teaching, or genetics is not really knowable, but also not really relevant.

I agree. It’s not really knowable where the difference comes from and it’s not really relevant. The point of my Blog@CACM post is: we can do better. If we can teach spatial ability and subitizing, two skills that have a much stronger claim to being innate than programming, then we can certainly teach people to program better.

If we follow common practice and it’s unsuccessful, it’s not surprising that we think, “I tried. I explained carefully. I gave interesting assignments. I gave good feedback. It’s got to be an innate trait. Some students are just born wired to program.”

I watch my children taking CS classes, along with English, Chemistry, Physics, and Biology classes. In the CS classes, they code. In the other classes, they do on-line interactive exercises, they write papers, they use simulations, they solve problems by-hand. Back in CS, the only activity is coding with feedback. If we only have one technique for teaching, we shouldn’t be surprised if it doesn’t always work

Here’s a reasonable hypothesis: We get poor results because we use ineffective teaching methods. If we want to teach CS more effectively, we need to learn and develop better methods. If we don’t strive for better methods, we’re not going to get better results.

A first step is to be more methodical with how we choose methods. In a 2011 paper by Davide Fossati and me (see here), we found that CS teachers generally don’t use empirical evidence when making changes in how we teach. We act from our intuition, but our students aren’t like us, and our intuition is not a good indicator of what our students need.

Next, we need to experiment with more methods. We want to get to a place where we identify known problems in our students’ understanding, and then used well-supported methods that help students develop more robust understandings. We probably don’t have a wide range of different techniques for teaching assignment, iteration, recursion, and similar concepts? We should try well-supported techniques like pair programming, peer instruction, or Media Computation (see CACM article on these). We should try to expand our techniques repertoire beyond simply grinding at code. We could try techniques like worked examples, Problets, CodingBat, games with learning outcomes like Wu’s Castle, multiple choice questions like in Gidget, the Parson’s Problems in the Runestone Interactive ebooks, or even computing without computers as in CS Unplugged.

We do not make it easy for CS teachers to pick up new, better, more proven methods. Sure, there are the SIGCSE Symposium proceedings, but that’s not a systematic presentation of what to use when. This is on the CS education research community to do better. But it’s also on the CS teaching community to demand better, to seek out better methods and studies of techniques.

If we taught better, there are a lot of problems in CS that we might impact. We might bring in a more diverse group of students. We might make our current students more successful. We might change attitudes about computing. Perhaps most importantly, maybe we as teachers will come to believe that we can teach anyone to program.

The 10K Hour Rule: Deliberate Practice leads to Expertise, and Teaching can trump Genetics

A recent article in Slate (see here) suggests that practice may not lead to expertise, that the “10,000 hour rule” is wrong. The “10,000 hour rule” was popularized by Malcolm Gladwell in his book Outliers (see excerpt here), but really comes from an important paper by K. Anders Ericsson and colleagues, “The Role of Deliberate Practice in the Acquisition of Expert Performance.” Ericsson claimed that 10,000 hours of deliberate practice results in expert-level performance.

The Slate article is based mostly on a new meta-analysis (see here) by Macnamara, Hambrick (also a co-author on the Slate article), and Oswald which reviewed and combined studies on expertise. They found that practice always was positively correlated with better performance, but did not explain all of (or even most of) the difference in expertise between study participants. The Slate article authors suggest, then, that deliberate practice is not as important as genetics or innate talent.

Deliberate practice left more of the variation in skill unexplained than it explained…There is now compelling evidence that genes matter for success, too…What all of this evidence indicates is that we are not created equal where our abilities are concerned.

The paper and article make two big mistakes that leave the “10,000 hour rule” as valid and valuable. The first is that practice is not the same as deliberate practice, and the second is that the fallback position can’t be genetics/innate talent. In general, their argument hinges on practice hours all being of equal value, which shows a lack of appreciation for the role of teaching.

Practice is not the same as deliberate practice

Ericsson was pretty clear in his paper that all practice is not created equal. Deliberate practice is challenging, focused on the skills that most need to be developed, with rapid feedback. (Here’s a nice blog post explaining deliberate practice.) Simply putting in 10,000 hours of practice in an activity does not guarantee expertise. Ericsson and the Slate authors would be in agreement on this point.

I’m sure that we’ve all seen musicians or athletes (and if we’re honest, we’ve probably all been like those musicians or athletes) who sometimes just “phone it in” during practice, or even during a game. I used to coach my daughters’ soccer teams, and I can absolutely assure you that there were hours in games and rehearsals where some of my players really didn’t make any progress. They found ways of getting through practice or games without really trying.

In the Macnamara paper, whether practice was “deliberate” or not was determined by asking people. They collected practice logs, surveys, and interviews. The participants in the studies self-reported whether the practice was deliberate. Imagine someone telling the interviewer or writing in their log, “Yeah, well, about 5,000 of those 10,000 hours, I was really lazy and not trying very hard.”  It’s impossible to really distinguish practice from deliberate practice in this data set.

The bottom-line is that the Macnamara study did not test Ericsson’s question. They tested a weak form of the “10,000 hour rule” (that it’s just “practice,” not “deliberate practice”) and found it wanting. But their explanation, that it’s genetics, is not supported by their evidence.

Genetics/Innate starts at birth, no later

The Slate authors argue that, if practice doesn’t explain expertise, then it must be genetics. They cite two studies that show that identical twins seem to have similar music and drawing talent compared to fraternal twins. But that’s correlation and doesn’t prove causation — there may be any number of things on which the identical twins aren’t similar. (See this great Radiolab podcast exploring these kinds of miraculous misconceptions.)

If you’re going to make the genetics/innate argument, you have to start tracking participants at birth. Otherwise, there’s an awful lot that might add to expertise that’s not going to get counted in any practice logs.

I took classes on how to coach soccer. One of the lessons in those classes was, “It’s a poor coach who makes all practices into scrimmage.” Rather, we were taught to have students do particular drills to develop particular skills. (Sound like deliberate practice?) For example, if my players were having trouble dribbling, I might have them dribble a ball in a line around cones, across distances, through obstacles.

Can you imagine a child who one day might play in a soccer team with official practices — but before those practices and perhaps even before joining a team might dribble a ball around the neighborhood? Wouldn’t that be developing expertise? And yet, it wouldn’t be counted in player logs or practice hours. A kid who did lots of dribbling might come into a team and seem like a superstar with all kinds of innate talent. One might think that the kid had the “Soccer gene.”

To start counting hours-towards-expertise anything later than birth is discounting the impact of learning in the pre-school years on up. We know that pre-school years make a difference (see this website that Diana Franklin sent me, and the argument for pre-school in this recent Freakonomics podcast). A wide variety of activities can develop skills that can be influence expertise. If you don’t start tracking students from birth, then it’s hard to claim that you’ve counted in the practice log everything that’s relevant for expertise.

The claim that expertise is determined at birth is a common claim among CS educators. Most CS teachers to whom I’ve asked the question are convinced some people “can’t” learn to code, that it’s genetic or innate to learn programming. That’s where the myth of the “Geek Gene” came from (Raymond Lister has written several times on that). Couldn’t it be that there are dribbling-around-the-neighborhood activities that lead toward CS expertise? Consider the famous pre-programming activity of writing the instructions out for making a peanut-butter-and-jelly sandwich (like here). If we believe that that kind of practice helps to develop CS expertise, then other “writing instructions out” activities might lead towards CS expertise. Maybe people who seem to have genetic/innate ability in CS just did a lot of those kinds of activities before they got to our classes.

The clock on developing expertise doesn’t start when students walk through our door.

Bigger than P=NP: Is teaching > genetics?

In the end, it’s very difficult to prove or disprove that genetics accounts for expertise in cognitive skill. I don’t think Macnamara et al. settled the score. But my point about deliberate practice actually points to a much bigger issue.

Teachers Matter is the two word title of a 2012 OECD report (available here). There is a difference between great teachers and poor teachers, and the difference can be seen in terms of student performance. If you believe that (and there’s gobs of evidence that says you should), then it seems obvious that all practice is not created equal. Hours spent in practice with a good teacher are going to contribute more to expertise than hours spent without a teacher. Look back at that definition of “deliberate practice” — who’s going to pick the activities that most address your needs or provide the immediate feedback? The definition of deliberate practice almost assumes that there’s going to be teacher in the loop.

An open question is just how far we can get with excellent teaching. How much can we use teaching to get beyond genetic disparities? Is teaching more powerful than genetics? That’s an important question, and far more important than the classic CS question whether P=NP. I believe that there are limits. There are genetic problems that teaching alone can’t address. But we don’t know what those limits are.

We certainly have evidence that we can use teaching to get past some differences that have been chalked up to genetics or being innate. Consider the fact that men have better spatial skills than women. Is it innate, or is it learned? It’s not clear (see discussion on that here). But the important point is: it doesn’t matter. Terlecki, Newcombe, and Little have found that they can teach women to perform as well as men on visual skills and that the improvements in spatial ability both transfers and persists (see the journal article version here). The point is that spatial skills are malleable, they can be developed. Why should we think that other cognitive skills aren’t? The claims of the Slate authors and Macnamara et al ignore the power of a great teacher to go beyond simple rote practice to create deliberate opportunities to learn. The words teach, teacher, and teaching don’t appear in either article.

Here’s my argument summarized.  The Slate authors and Macnamara et al. dismiss the 10K hour rule too lightly, and their explanation of genetic/innate basis for expertise is too simple.  Practice is not the same as deliberate practice, or practice with a teacher. Expertise is learned, and we start learning at birth with expertise developing sometimes in ways not directly connected to the later activity. The important part is that we are able to learn to overcome some genetic/innate disparities with good teaching. We shouldn’t be giving up on developing expertise because we don’t have the genes. We should be thinking about how we can teach in order to develop expertise.

What Computing Education Research does that Engineering Ed and Physics Ed Research doesn’t

In my most recent recent Blog@CACM post on last month’s ACM Ed Council meeting, I mentioned that I gave a talk about the differences between computing education research and engineering education research (EER) and physics education research (PER). Let me spell these out a bit here.

The context was a panel on how to grow computing education research (CER). We were asked to consider the issue of getting more respect for computing education research (an issue I’ve written on before). I decided to explore the characteristics of CER that are important and that are not present in EER or PER. Engineering Education Research (EER) and Physics Education Research (PER) are better established and more well-respected in the United States.  But I’ve come to realize that CER has characteristics that are different from what’s in EER and PER.

Engineering Education Research

I came to a new understanding of EER because of a cross-campus STEM Education Research seminar that we’re holding at Georgia Tech this semester. It’s given me the opportunity to spend a couple hours each week with people who publish in Journal of Engineering Education (see here), review for them, and edit for them. JEE is generally considered to top EER journal.

If you’re not familiar, engineering education research is a big deal in the United States. There are well-funded engineering education research centers. There are three academic departments of EER. It’s well-established.

In one of the early sessions, we talked about the McCracken Study (Mike McCracken has been coming to the sessions, which has been great), where an experimental assignment was used in five classes in four countries. Are there similar studies in EER? Our EER colleagues looked at one another and shrugged their shoulders. For the most part, EER studies occur in individual classes at individual institutions. Laboratory studies are rare. International collaborations are really rare.

I started digging into JEE. The last issue of JEE only had papers by American authors from American institutions. I’m digging further back. My colleagues are right — international authors and collaborations are unusual in JEE.

In contrast, I don’t think that the ACM Transactions on Computing Education has ever had an only-American issue. Our ICER conference is not even American-dominated. The ICER 2014 best paper award went to a paper by Leo Porter (American) who worked with Raymond Lister (Australian) using data collected from Daniel Zingaro’s classroom (at U. Toronto in Canada) to address a theory by Anthony Robins (New Zealand).  We use classroom studies, laboratories studies, and frequently use multi-institutional, multi-national (MIMN) collaborative studies (and study how to conduct them well).

Physics Education Research

At the January workshop on CER that Steve Cooper organized (paper to appear in CACM next month — it’s where Eric Roberts gave a keynote that I wrote about here), Carl Wieman was the opening keynote speaker. He talked about the hot issues in physics education research.

After his talk, he was asked about how physics education researchers were dealing with the gender skew in physics and about improving access in K-12 to quality educational opportunities. If you look at Brian Danielak’s visualization of AP CS test data, you’ll see that CS is the most gender-skewed, but Physics follows closely after.  (Click on the picture to get a bigger version, and look at the lower left-hand corner.)

 

Carl said that gender diversity just wasn’t a priority in PER. I dug into the PER groups around the US. From what I could find, he’s right. Eric Mazur’s group has one paper on this issue, from 2006 (see here). I couldn’t find any at U. Washington or at Boulder. There probably is work on gender diversity in physics education research, but it certainly doesn’t stand out like the broadening participation in computing effort in the United States (see papers listing from Google Scholar).  The January workshop really brought home for me that a key characteristic of CER, particularly in comparison with PER, is an emphasis on broadening participation, on social justice, on improving the diversity of the field, and guaranteeing access to quality educational opportunities for all.

I don’t have a deep bottom-line here. It was only a few minute talk. My exploration of EER and PER gave me a new appreciation that CER has something special. It’s not as big or established as EER or PER, but we’re collaborative, international, working on hard and important problems, and using a wide variety of methods, from in-classroom to laboratory studies. That’s pretty cool.

Where AP CS is taught in Georgia and California, and where there is none at all

April Heard at Georgia Tech built this map for us about where AP CS is taught in the state of Georgia.  Some of it is totally to be expected. Most of the schools are in the Atlanta region, with a couple in Columbus, a handful in Macon, and a few more in Augusta and Savannah area.

But what’s disappointing is that huge swath in the south of the state with nothing.  Not a single school south of Columbus and west of Brunswick.  In terms of area, it’s about 1/3 of the state.  Albany is home to Albany State University, the largest HBCU in Georgia.  No AP CS at all there.  And Georgia is one of the top states for having AP CS.

Sure, there might be some non-AP CS teachers in South Georgia, but we’re talking a handful.  Not double, and certainly not a magnitude more than AP CS.

I suspect that much of the US looks like this, with wide stretches without a CS teacher in sight.  April is continuing to generate these maps for states that we’re working with in ECEP.  Here’s California, with big empty stretches.

Tom McKlin just generated this new map, which overlays the AP CS teacher data on top of mean household income in a school district.  The correlation is very high — districts with money have AP CS, and those that don’t, don’t.

 

Creating CS Meetups for Constructionist Adult Education

A few months ago, I wrote a post on Constructionism for Adults. I argued that we want constructionist learning for adults, but most constructionist learning environments are aimed at children. I suggested that adults have three challenges in constructionism that kids don’t have:

• Adults have a “face” (in the Goffman sense) that they want to preserve.
• Adults don’t necessarily have expertise in an area, but as adults, they are presumed to have expertise.
• Adults have less free time and more responsibilities than children.

I mentioned in that post that I was learning to play the ukulele, and that that experience was leading to new insights for me about adult education. I’m going to continue to use my ukulele learning to suggest a way to create constructionist learning opportunities for adults.

Legitimate Peripheral Participation for Adult Learning

From this point of view a very remarkable aspect of the Samba School is the presence in one place of people engaged in a common activity – dancing – at all levels of competence from beginning children who seem scarcely yet able to talk, to superstars who would not be put to shame by the soloists of dance companies anywhere in the world. The fact of being together would in itself be “educational” for the beginners; but what is more deeply so is the degree of interaction between dancers of different levels of competence. From time to time a dancer will gather a group of others to work together on some technical aspect; the life of the group might be ten minutes or half an hour, its average age five or twenty five, its mode of operation might be highly didactic or more simply a chance to interact with a more advanced dancer. The details are not important: what counts is the weaving of education into the larger, richer cultural-social experience of the Samba School.

So we have as our problem: to transfer the positive features of the Samba School into the context of learning traditional “school material” — let’s say mathematics or grammar. Can we solve it?

— Seymour Papert, “Some Poetic and Social Criteria for Education Design” (1975)

What Seymour was seeing in Samba schools is what Jean Lave and Etienne Wenger called a community of practice. My colleagues Jose Zagal and Amy Bruckman have a wonderful paper describing how Samba schools are a form of a community of practice, and how that model appears in the Computer Clubhouses that Yasmin writes about in her new book. In their influential 1991 book Situated learning: Legitimate Peripheral Participation, Lave and Wenger described several examples for how learning occurs in everyday settings, often with adults. Lave and Wenger point out

• There are the midwives who train their daughters who start out just going-along to help mother at births.
• There are the tailors who start out by delivering fabric and pieces between shops, and in that way, get to see many shops — without actually doing tailoring but still doing something useful to being a tailor.
• There are the attendees at Alcoholics Anonymous meetings who learn to tell their stories through listening to role models and getting feedback from others.

There are some key elements to these stories:

• Newcomers start out doing something useful, but on the periphery of the community — hence, legitimate peripheral participation. Jose and Amy point out that successful Samba schools are flexible to outsiders (anyone can become a newcomer).
• Everyone sees practice (story-telling, being a tailor, helping a birth, dancing at Samba school) at different levels. Jose and Amy talk about having a diversity of membership (socio-economic, age, race, and expertise) and that there are events for public to exhibit practice.
• There are some members of the community of practice who are clearly at the center. They serve as role models for others. From the newcomers to those practicing but not yet central, everyone strives to learn to become like those at the center of the community of practice.

Ukulele Meet-up As Samba School and Community of Practice

In my quest to learn to play ukulele, I’ve joined the Southeast Ukers, a group of ukulele players in Atlanta. I was fortunate to know a Uker who invited me to a meet-up. A meet-up is the experience I’ve had that is closest to how I understand a Samba school.

The meet-up is held at a local Hawaiian BBQ restaurant at 2 pm on the 1st and 3rd Sunday’s in a month. Ukers show up with a couple of Ukulele songbooks with literally hundreds of songs. (I happened to have one of them on my iPad when I first went, and had both by my second meet-up.)

For the first 90 minutes, it’s a “strum-along.” The leader calls out a page number, then after a count off, everyone plays the same song and sings along. This is a remarkably successful learning activity for me as a newcomer.

• It’s completely safe. If I can play along, I do. If I can’t, I just sing, or just watch. If I can play the chords but more slowly, I catch up on the second or third strum of a measure. I can immediately hear if I’m getting it right (right chord, right rhythm) or if I made a mistake. The people right next to me can hear me and can comment on my playing, but only those — it’s a big group.
• It’s a public opportunity for learning. I know what chords everyone is playing. I can look around and see how everyone else plays it.
• While everyone is strumming, the really good players are picking individual notes, or doing tricky rhythms. I can hear those, and watch them do it, and develop new goals for things I want to learn.

The gaps between the songs are when a lot of the learning happens for me. I get coaching (e.g., “You are doing really well!” or “I heard you stammer in your rhythm on that hard chord change”). I can ask specific questions and get specific advice. I’ve received tips on how to make D7 chords more easily, and different ways to do barre chords.

After 90 minutes, it’s open-mic time. Individual ukers sign up during the strum-along, and then go up to the corner stage to perform (a quality setup, with separate mics for singing and for playing and someone at a sound board). Here’s where we get to see those on their way or at the center of the community of practice. Those at the center of the community of practice reference other meet-ups and other performances, and often play their own compositions.

As a newcomer, I stare slack-jawed at the open-mic performances. They create music that I didn’t know could be made on a ukulele. Slowly, I’m starting to imagine myself playing at open-mic, even writing my own music. I’m starting to set a personal goal to become more central to this community of practice.

At a meet-up, I talk to my fellow ukers and get a sense of how much effort does it take to develop that level of expertise. I start to get a sense of how much effort it will take me to reach different levels of expertise. There’s no expectations set on me, and no presumption of expertise. I can decide for myself on how good I want to get and how much effort I can afford to put in. I can set my own pace for when I might one day sign up for an open-mic performance, and maybe even try to compose my own music. (But it won’t be soon.)

Creating a Computing Samba/Meet-Up Culture

Could we create an experience like the Samba school or like the meet-up for learning computing by adults, like undergraduates, end-user programmers, and high school teachers? What are the critical parts that we would need to duplicate?

It must be safe. People should be able to save face at the meet-up. Participants need to be able to talk with one another privately, without overhead (e.g., learning some complicated mechanism to open a private chat line). Newcomers need to be able to participate without expectation or responsibility, but be able to take on expectation and responsibility as they become more central to the community.

There must be legitimate peripheral participation. Newcomers have to be able to participate in a way that’s meaningful while working at the edge of the community of practice. Asking the noobs in an open-source project to write the docs or to do user testing is not a form of legitimate peripheral participation because most open source projects don’t care about either of those. The activity is not valued.

Everyone’s work must be visible. Newcomers should be able to see the great work of the more central participants just by looking around. This is probably the trickiest part. We tend to confuse accessibility with visibility. Yes, on an open source project, everyone’s contributions are accessible — if you can figure out github, and figure out which files are meaningful, and figure out who contributed which. Visible means that you can look around without overhead and see what’s going on.

I must be able to work alone. Everyone needs a lot of hours of practice to develop expertise. It can’t happen just in the meetup. There needs to be a way to develop one’s work alone, and share it in the meetup.

A Proposed Computing Meet-Up Context

Here are some early thoughts on what it might be like to create an environment for learning computing the way that the ukulele meetup works.

Years ago, the Kansas environment was implemented in the programming language Self. Kansas was remarkable. It was a shared desktop where all participants could see each other, see their cursors, and see their developing work.

Lex Spoon created a version of Kansas for the Squeak programming language called Nebraska (for another “large, flat, sparsely-populated space”). Nebraska in Squeak is particularly interesting for a meet-up because all the rich multi-media features of Squeak are available in both a programmable and a drag-and-drop form.

Here’s a sketch of what I propose, using a shared space like Kansas or Nebraska:

• Participants come to a physical space with their laptops. Physical co-location is key for safe and easy peer communication. A new journal article on co-located viewing of MOOCs suggests that co-location may dramatically improve learning.
• The participants log on to a shared Kansas/Nebraska server, which is displayed an ultra-high resolution display.
• The participants work together to create a multimedia show.
  • Newcomers can build the graphical or audio elements (perhaps some developed at home and brought to the meetup). Building can start in drag-and-drop form, but can develop into code elements. If something doesn’t work, it might not make it into the show, but it’s a contribution to the shared space, and it’s visible for comment and review.
  • All participants can watch others work, and can walk over to them to ask questions.
  • Participants can specialize, by focusing on different aspects of the performance (e.g., music, graphics, layout, synchronization).
  • Those more central to the community can assemble components and choreograph the whole performance (much as in a Samba school).

Would this kind of meet-up be a way for adults to learn computation in a constructionist manner?