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The Growing Tide of Anti-Intellectualism

Mark Guzdial:

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.

Originally posted on 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 1,026 more words

October 20, 2014 at 11:07 am 1 comment

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.

October 20, 2014 at 8:25 am Leave a comment

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.

October 18, 2014 at 8:33 am 1 comment

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.

October 17, 2014 at 12:30 pm Leave a comment

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:

Is_There_a_Crisis_in_Computer-Science_Education__–_Data_Points_-_Blogs_-_The_Chronicle_of_Higher_Education

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.

October 16, 2014 at 8:48 am Leave a comment

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.

October 15, 2014 at 8:32 am 27 comments

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.

October 13, 2014 at 8:21 am 16 comments

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