Archive for May, 2012
I liked Alfred’s sentiment in this post: “Be what’s next!” The issues around my BLS post remind me of this idea. We don’t want to give up on what we have always done, because we want to retain those outcomes. But what if those outcomes are less useful today? “Be what’s next!”
Grace Hopper used to tell her audiences that if they ever used “because we have always done it this way” as an excuse for anything that she would magically appear next to them to “haunt” them. I first heard her say that some 40 years ago and it has stuck with me since then. And yet people do use that as an excuse. Oh they may say it differently but that is what they often mean. In computer science education all too often people believe that because they learned computer science some way that everyone should learn it that way. It’s not as bad as it used to be but at times I wonder if people are just saying it differently. For example “we use command line application programming because we don’t want students getting too wrapped up in GUI stuff.” Or perhaps “we need students to use text editors and command line compilers so that the really understand what is going on.” Baloney I say. Use modern tools and let students create applications that are real looking and relevant to them. In the long run this will be more incentive to learn more than anything else.
CalArts Awarded National Science Foundation Grant to Teach Computer Science through the Arts | CalArts
Boy, do I want to learn more about this! Chuck and Processing, and two semesters — it sounds like Media Computation on steroids!
The National Science Foundation (NSF) has awarded California Institute of the Arts (CalArts) a grant of $111,881 to develop a STEM (Science, Technology, Engineering and Mathematics) curriculum for undergraduate students across the Institute’s diverse arts disciplines. The two-semester curriculum is designed to teach essential computer science skills to beginners. Classes will begin in Fall 2012 and are open to students in CalArts’ six schools—Art, Critical Studies, Dance, Film/Video, Music and Theater.
This innovative arts-centered approach to teaching computer science—developed by Ajay Kapur, Associate Dean of Research and Development in Digital Arts, and Permanent Visiting Lecturer Perry R. Cook, founder of the Princeton University Sound Lab—offers a model for teaching that can be replicated at other arts institutions and extended to students in similar non-traditional STEM contexts.
I did my monthly post at Blog@CACM on the some of the recent data on how few women there were in computing. I suggested that things haven’t got better in the last 10 years because we really haven’t decided that there’s a problem with under-representation. The comments to that post suggest that I’m right. Blog@CACM posts don’t often get comments. Three in a week is a lot, and two of those expressed the same theme, “Women are choosing not to go into IT. Why is that a problem?” It’s a problem because there are too few people in IT, and there are many women who could do the work that we should be trying to recruit, motivate, and engage, even if it requires us to change our own cultures and careers. Computing has a bright future, and I predict that most applications of computing in our lives are still to be invented. We need a diverse range of people to meet that future, and change in our culture and careers would be healthy.
The situation is different with respect to academia. The article linked below points out that women are turned off to careers in academia are greater rates than men. Other recent work suggests that students in doctorate programs lose interest in academia the longer that they are in it. There should be more women in academia, and academia cultures and careers should change to be more attractive to a broader range of qualified applicants. But what could make that happen?
In contrast to the computing industry, academia isn’t growing. The economics in academia are changing, and there will be fewer academic jobs (especially in CS). I still believe that we ought to ramp up CS faculty hiring, in order to offer computing to more people (even everyone) on campus, but the economics and organizational trends are against me. If we were to hire in academia, we should make an effort to draw in more women and more under-represented minorities. We absolutely should strive to improve the culture and career prospects in academia to retain the (relatively little) diversity that we now have in academia. But neither hiring nor retention are at the top of academia’s concerns right now. Maybe the young scientists are wise to seek other opportunities, and PhD students are figuring out that academia may not hold great career prospects?
Young women scientists leave academia in far greater numbers than men for three reasons. During their time as PhD candidates, large numbers of women conclude that (i) the characteristics of academic careers are unappealing, (ii) the impediments they will encounter are disproportionate, and (iii) the sacrifices they will have to make are great.
Men and women show radically different developments regarding their intended future careers. At the beginning of their studies, 72% of women express an intention to pursue careers as researchers, either in industry or academia. Among men, 61% express the same intention.
By the third year, the proportion of men planning careers in research had dropped from 61% to 59%. But for the women, the number had plummeted from 72% in the first year to 37% as they finish their studies.
I’ve raised this question before, but since I just saw Nora Newcombe speak at NCWIT, I thought it was worth raising the issue again. Here’s my picture of one of her slides — could definitely have used jitter-removal on my camera, but I hope it’s clear enough to make the point.
This is from a longitudinal study, testing students’ visual ability, then tracking what fields they go into later. Having significant visual ability most strongly predicts an Engineering career, but in second place (and really close) is “Mathematics and Computer Science.” That score at the bottom is worth noting: Having significant visual ability is negatively correlated with going into Education. Nora points out that this is a significant problem. Visual skills are not fixed. Training in visual skills improves those skills, and the effect is durable and transferable. But, the researchers at SILC found that teachers with low visual skills had more anxiety about teaching visual skills, and those teachers depressed the impact on their students. A key part of Nora’s talk was showing how the gender gap in visual skills can be easily reduced with training (relating to the earlier discussion about intelligence), such that women perform just as well as men.
The Spatial Intelligence and Learning Center (SILC) is now its sixth year of a ten year program. I don’t think that they’re going to get to computer science before the 10th year, but I hope that someone does. The results in mathematics alone are fascinating and suggest some significant interventions for computer science. For example, Nora mentioned an in-press paper by Sheryl Sorby showing how teaching students how to improve their spatial skills improved their performance in Calculus, and I have heard that she has similar results about computer science. Could we improve learning in computer science (especially data structures) by teaching spatial skills first?
Check out “Gas station without pumps” for more on the Next Generation Science Standards, available now for comment (but only through this week). There is a bit of computational thinking and computing education in there, but buried (as the blog post points out). I know that there is a developing effort to get more computation in there.
The first public draft of the Next Generation Science Standards is available from May 11 to June 1. We welcome and appreciate your feedback. [The Next Generation Science Standards]
Note that there are only 3 weeks given for the public review of this draft of the science standards, and that time is almost up. I’ve not had time to read the standards yet, and I doubt that many others have either. We have to hope that someone we respect has enough time on their hands to have done the commenting for us (but the people I respect are all busy—particularly the teachers who are going to have to implement the standards—so who is going to do the commenting?).
I’m also having some difficulty finding a document containing the standards themselves. There are clear links to front matter, how to interpret the standards, a survey for collecting feedback, a search interface, and various documents about the standards, but I had a hard time finding a simple link to a single document containing all the standards. It was hidden on their search page, rather than being an obvious link on the main page.
Low-income students and schools are getting better, according to this study. They’re just getting better so much more slowly than the wealthy students and schools. Both are getting better incrementally (both moving in the right direction), but each increment is bigger for the rich (acceleration favors the rich).
We heard something similar from Michael Lach last week. The NSF CE21 program organized a workshop for all the CS10K efforts focused on teacher professional development. It was led by Iris Weiss who runs one of the largest education research evaluation companies. Michael was one of our invited speakers, on the issue of scaling. Michael has been involved in Chicago Public Schools for years, and just recently from a stint at the Department of Education. He told us about his efforts to improve reading, math, and science scores through a focus on teacher professional development. It really worked, for both the K-8 and high school levels. Both high-SES (socioeconomic status) and low-SES students improved compared to control groups. But the gap didn’t get smaller.
Despite public policy and institutional efforts such as need-blind financial aid and no-loan policies designed to attract and enroll more low-income students, such students are still more likely to wind up at a community college or noncompetitive four-year institution than at an elite university, whether a member of the Ivy League or a state flagship.The study, “Running in Place: Low-Income Students and the Dynamics of Higher Education Stratification,” will be published next month in Educational Evaluation and Policy Analysis, but an abstract is already available on the journal’s website.“I think [selective colleges] very much want to bring in students who are low-income, for the most part,” said Michael N. Bastedo, the study’s lead author and an associate professor of higher education at the University of Michigan. “The problem is, over time, the distance between academic credentials for wealthy students and low-income students is getting longer and longer…. They’re no longer seen as competitive, and that’s despite the fact that low-income students are rising in their own academic achievement.”
This week at the NCWIT Summit, I heard Joshua Aronson speak on stereotype threat. I’ve read (and even taught) about stereotype threat before, but there’s nothing like hearing the stories and descriptions from the guy who co-coined the term. Stereotype threat is “apprehension arising from the awareness of a negative stereotype or personal reputation in a situation where the stereotype or identity is relevant, and thus comparable.” Aaronson has lots of examples. Remind women of the gender (and implicitly, of the stereotype that says women are worse than men at math) and their scores drop on math tests. Remind African Americans of their race (and implicitly, of the stereotype about African Americans and intelligence) and their scores on IQ tests drop.
I took a picture of one of Aronson’s slides. He observed that most of the tests in the laboratory experiments were, well, laboratory experiments. They weren’t “real,” that is, they didn’t count for anything. So what if we tweaked the AP Calculus test? Typically, the AP Calc asks students their gender just before they start the test, which makes the stereotypes about gender salient. What if you moved that question to the end of the test? Here are the results:
If you ask before, women do much worse than men, as past results have typically shown. If you ask after, the women do better than the men, but the men also do much worse than before! Reminding men of their gender, and the stereotype, improves their performance. Don’t remind them, and they do worse. Which leaves us in a tough position: When should you ask gender?
Now, there is a solution here: Dweck’s fixed vs growth mindset. Many children believe that intelligence is a fixed quantity, so if they do badly at something, they believe that they can’t do better later with more work. What if we emphasize that intelligence is malleable? Writes Dweck in Brainology:
The wonderful thing about research is that you can put questions like this to the test — and we did (Kamins and Dweck, 1999; Mueller and Dweck, 1998). We gave two groups of children problems from an IQ test, and we praised them. We praised the children in one group for their intelligence, telling them, “Wow, that’s a really good score. You must be smart at this.” We praised the children in another group for their effort: “Wow, that’s a really good score. You must have worked really hard.” That’s all we did, but the results were dramatic. We did studies like this with children of different ages and ethnicities from around the country, and the results were the same.
Here is what happened with fifth graders. The children praised for their intelligence did not want to learn. When we offered them a challenging task that they could learn from, the majority opted for an easier one, one on which they could avoid making mistakes. The children praised for their effort wanted the task they could learn from.
The children praised for their intelligence lost their confidence as soon as the problems got more difficult. Now, as a group, they thought they weren’t smart. They also lost their enjoyment, and, as a result, their performance plummeted. On the other hand, those praised for effort maintained their confidence, their motivation, and their performance. Actually, their performance improved over time such that, by the end, they were performing substantially better than the intelligence-praised children on this IQ test.
Aronson and colleagues asked in their Department of Education report: “Does teaching students to see intelligence as malleable or incrementally developed lead to higher motivation and performance relative to not being taught this theory of intelligence?” They did find that teaching a growth mindset really did result in higher motivation and performance. They recommended the strategy, “Reinforce for students the idea that intelligence is expandable and, like a muscle, grows stronger when worked.”
It turns out that, if you teach students about growth mindset, then they are less likely to be influenced by stereotype threat. Dweck writes in her Brainology essay:
Joshua Aronson, Catherine Good, and their colleagues had similar findings (Aronson, Fried, and Good, 2002; Good, Aronson, and Inzlicht, 2003). Their studies and ours also found that negatively stereotyped students (such as girls in math, or African-American and Hispanic students in math and verbal areas) showed substantial benefits from being in a growth-mindset workshop. Stereotypes are typically fixed-mindset labels. They imply that the trait or ability in question is fixed and that some groups have it and others don’t. Much of the harm that stereotypes do comes from the fixed-mindset message they send. The growth mindset, while not denying that performance differences might exist, portrays abilities as acquirable and sends a particularly encouraging message to students who have been negatively stereotyped — one that they respond to with renewed motivation and engagement.
Dweck is pretty careful in how she talks about intelligence, but some of the others are not She talks about “while not denying that performance differences might exist” and “portrays abilities as acquirable” (emphasis mine). The Dept of Ed report says we should tell students that “intelligence is expandable.” Is it? Is intelligence actually malleable?
The next workshop I went to after Aronson’s was Christopher Chabris’s on women and the collective intelligence of human groups. Chabris showed fascinating work that the proportion of women in groups raises the collective intelligence of groups. But before he got into his study, he talked about personal and collective intelligence. He quoted Charles Spearman from 1904: “Measurements of cognitive ability tend to correlate positively across individuals.” Virtually all intelligence tests correlate positively, which suggests that they’re measuring the same thing, the same psychological construct. What’s more, Chabris showed us that the variance in intelligence can be explained in terms of physical structures of the brain. Personal intelligence is due to physical brain structures, but we can work collectively to do more and think better.
My Georgia Tech colleague, Randy Engle, was interviewed in the NYTimes a few weeks ago, arguing that intelligence is fixed. It’s due to unchanging physical characteristics of the brain. We can’t change it.
For some, the debate is far from settled. Randall Engle, a leading intelligence researcher at the Georgia Tech School of Psychology, views the proposition that I.Q. can be increased through training with a skepticism verging on disdain. “May I remind you of ‘cold fusion’?” he says, referring to the infamous claim, long since discredited, that nuclear fusion could be achieved at room temperature in a desktop device. “People were like, ‘Oh, my God, we’ve solved our energy crisis.’ People were rushing to throw money at that science. Well, not so fast. The military is now preparing to spend millions trying to make soldiers smarter, based on working-memory training. What that one 2008 paper did was to send hundreds of people off on a wild-goose chase, in my opinion.
“Fluid intelligence is not culturally derived,” he continues. “It is almost certainly the biologically driven part of intelligence. We have a real good idea of the parts of the brain that are important for it. The prefrontal cortex is especially important for the control of attention. Do I think you can change fluid intelligence? No, I don’t think you can. There have been hundreds of other attempts to increase intelligence over the years, with little or no — just no — success.”
Is intelligence expandable and malleable, or is it physical and fixed? There is a level where it doesn’t matter. Telling students that intelligence is expandable and malleable does have an effect. It results in higher test scores and better performance. But on the other hand, is it good policy to lie to students, if we’re wrong about the malleability?
Maybe we’re talking about different definitions of “intelligence.” Engle and Chabris may be talking about a core aspect of intelligence that is not malleable, and Dweck and Aronson may be talking about knowledge, skills, and even metacognitive skills that can be grown throughout life. But we say that “intelligence” is malleable, and the work in stereotype threat tells us that the language matters. What words we use, and how (and when) we prompt students impacts performance. If we don’t say “intelligence can be grown like a muscle” and instead say, “knowledge and skills are expandable and malleable,” would we still get the same benefits?
I’m not a psychologist. When I was an education graduate student, I was told to think about education as “psychology engineering.” Educators take the science of psychology into actual practice to create learning systems and structures. I look to the psychology to figure out how to help students learn. While Dweck and Aronson are explicitly giving educators strategies that really work, I worry about the conflict I see between them and other psychologists in terms of the basic science. Is it a good strategy to get positive learning effects by telling students something that may not be true?