Posts tagged ‘undergraduates’
Thought-provoking piece on NPR. Take parents who believe that the MMR vaccine causes autism. Show them the evidence that that’s not true. They might tell you that they believe you — but they become even less likely to vaccinate future children. What?!?
The explanation (quoted below) is that these parents found a sense of identity in their role as vaccine-deniers. They rejected the evidence at a deeply personal level, even if they cognitively seemed to buy it.
I wonder if this explains a phenomenon I’ve seen several times in CS education: teaching with a non-traditional but pedagogically-useful tool leads to rejection because it’s not the authentic/accepted tool. I saw it as an issue of students being legitimate peripheral participants in a community of practice. Identity conflict offers a different explanation for why students (especially the most experienced) reject Scheme in CS1, or the use of IDE’s other than Eclipse, or even CS teacher reaction when asked not to use the UNIX command line. It’s a rejection of their identity.
An example: I used to teach object-oriented programming and user interface software using Squeak. I had empirical evidence that it really worked well for student learning. But students hated it – especially the students who knew something about OOP and UI software. “Why aren’t we using a real language? Real OOP practitioners use Java or C++!” I could point to Alan Kay’s quote, “I invented the term Object-Oriented, and I can tell you I did not have C++ in mind.” That didn’t squelch their anger and outrage. I’ve always interpreted their reaction to the perceived inauthenticity of Squeak — it’s not what the majority of programmers used. But I now wonder if it’s about a rejection of an identity. Students might be thinking, “I already know more about OOP than this bozo of a teacher! This is who I am! And I know that you use Java or C++!” Even showing them evidence that Squeak was more OOP, or that it could do anything they could do in Java or C++ (and some things that they couldn’t do in Java or C++) didn’t matter. I was telling them facts, and they were arguing about identity.
What Nyhan seems to be finding is that when you’re confronted by information that you don’t like, at a certain level you accept that the information might be true, but it damages your sense of self-esteem. It damages something about your identity. And so what you do is you fight back against the new information. You try and martial other kinds of information that would counter the new information coming in. In the political realm, Nyhan is exploring the possibility that if you boost people’s self-esteem before you give them this disconfirming information, it might help them take in the new information because they don’t feel as threatened as they might have been otherwise.
Great to see Dan Garcia and his class getting this kind of press! I’m not sure I buy the argument that SFGate is making, though. Do female students at Berkeley find out about this terrific class and then decide to take it? Or are they deciding to take some CS and end up in this class? Based on Mike Hewner’s work, I don’t think that students know much about the content of even great classes like Dan’s before they get there.
It is a predictable college scene, but this Berkeley computer science class is at the vanguard of a tech world shift. The class has 106 women and 104 men.
The gender flip first occurred last spring. It was the first time since at least 1993 – as far back as university enrollment records are digitized – that more women enrolled in an introductory computer science course. It was likely the first time ever.
It’s a small but a significant benchmark. Male computer science majors still far outnumber female, but Prof. Dan Garcia’s class is a sign that efforts to attract more women to a field where they have always been vastly underrepresented are working.
“We are starting to see a shift,” said Telle Whitney, president of the Anita Borg Institute for Women and Technology.
Great interview with Stanford’s Mehran Sahami. I think he has his finger on what’s influencing students going into CS today.
And now a lot more students everywhere are choosing to major in computer science.
In terms of that trend turning around, part of it is the recovery in the high-tech economy, part of it is a change in perception. When people see companies like Google and Facebook being founded by relatively young people, they feel empowered and think: I can do that. And there’s the realization that the demand for computing, at least looking out over the next ten years, is certainly going to be there.
What are the factors that are still holding students back from studying computer science?
The problem is the educational opportunities. You take your average high school, and kids have several years of math classes, they have several years of science classes, several years of English, options for various kinds of vocational training, or history, or economics. But very few schools actually offer real computer science classes. So students don’t get the exposure in high school, of those who go to college, some have never considered computing before because they don’t really know what it is. One of the phenomena we see at Stanford is that the vast majority of our students, 90 percent of undergrads, take computer science classes even though there’s no requirement to do so. Some of them take it and end up loving it, but it’s too late to major in computer science. Had they been exposed to computer science earlier on, they could’ve started at a point that would allow them to pursue this as a major and as a career. When you take your first class your senior year and realize you love it, but you’re going to graduate in another quarter, you can’t complete a major. If there are more of those opportunities earlier in the pipeline, it will help address this.
I found the analysis linked below interesting. Most IT workers do not have an IT-related degree. People with CS degrees are getting snapped up. The suggestion is that there’s not a shortage of IT workers, because IT workers are drawn from many disciplines. There may be a shortage of IT workers who have IT training.
IT workers, who make up 59 percent of the entire STEM workforce, are predominantly drawn from fields outside of computer science and mathematics, if they have a college degree at all. Among the IT workforce, 36 percent do not have a four-year college degree; of those who do, only 38 percent have a computer science or math degree, and more than a third (36 percent) do not have a science or technology degree of any kind. Overall, less than a quarter (24 percent) of the IT workforce has at least a bachelor’s degree in computer science or math. Of the total IT workforce, two-thirds to three-quarters do not have a technology degree of any type (only 11 percent have an associate degree in any field).4
Although computer science graduates are only one segment of the overall IT workforce, at 24 percent, they are the largest segment by degree (as shown in Figure F, they are 46 percent of college graduates entering the IT workforce, while nearly a third of graduates entering IT do not have a STEM degree). The trend in computer scientist supply is important as a source of trained graduates for IT employers, particularly for the higher-skilled positions and industries, but it is clear that the IT workforce actually draws from a pool of graduates with a broad range of degrees.
CS researchers have long been interested in what predicts success in introductory computing, e.g., the “camel has two humps” paper, and the Bennedsen and Caspersen review of the literature. Would knowing who might succeed or fail allow us to boost retention? A new system at Purdue was claimed to do exactly that, but turns out, isn’t.
Michael Caulfield, director of blended and networked learning at Washington State University at Vancouver, decided to take a closer look at Signals after Purdue in a September press release claimed taking two Signals-enabled courses increased students’ six-year graduation rate by 21.48 percent. Caulfield described Purdue research scientist Matt Pistilli’s statement that “two courses is the magic number” as “maddening.”
Comparing the retention rates of the 2007 and 2009 cohorts, Caulfield suggested much of what Purdue described as data analysis just measured how many courses students took. As Signals in 2008 left its pilot and more students across campus enrolled in at least one such course, Caulfield found the retention effect “disappeared completely.”
Put another way, “students are taking more … Signals courses because they persist, rather than persisting because they are taking more Signals courses,” Caulfield wrote.
Karen Head has finished her series on how well the freshman-composition course fared (quoted and linked below), published in The Chronicle. The stats were disappointing — only about 238 of the approximately 15K students who did the first homework finished the course. That’s even less than the ~10% we saw completing other MOOCs.
Georgia Tech also received funding from the Gates Foundation to trial a MOOC approach to a first year of college physics course. I met with Mike Schatz last Friday to talk about his course. The results were pretty similar: 20K students signed up, 3K students completed the first assignment, and only 170 finished. Mike had an advantage that Karen didn’t — there are standardized tests for measuring the physics knowledge he was testing, and he used those tests pre-post. Mike said the completers fell into three categories: those who came in with a lot of physics knowledge and who ended with relatively little gain, those who came in with very little knowledge and made almost no progress, and a group of students who really did learn alot. They don’t know why nor the relative percentages yet.
The researchers also say, perhaps unsurprisingly, that what mattered most was how hard students worked. “Measures of student effort trump all other variables tested for their relationships to student success,” they write, “including demographic descriptions of the students, course subject matter, and student use of support services.”
It’s not surprising, but it is relevant. Students need to make effort to learn. New college students, especially first generation college students (i.e., whose parents have never gone to college), may not know how much effort is needed. Who will be most effective at communicating that message about effort and motivating that effort — a video of a professor, or an in-person professor who might even learn your name?
As Gary May, our Dean of Engineering, recently wrote in an op-ed essay published in Inside Higher Ed, “The prospect of MOOCs replacing the physical college campus for undergraduates is dubious at best. Other target audiences are likely better-suited for MOOCs.”
On the freshman-composition MOOC, Karen Head writes:
No, the course was not a success. Of course, the data are problematic: Many people have observed that MOOCs often have terrible retention rates, but is retention an accurate measure of success? We had 21,934 students enrolled, 14,771 of whom were active in the course. Our 26 lecture videos were viewed 95,631 times. Students submitted work for evaluation 2,942 times and completed 19,571 peer assessments (the means by which their writing was evaluated). However, only 238 students received a completion certificate—meaning that they completed all assignments and received satisfactory scores.
Our team is now investigating why so few students completed the course, but we have some hypotheses. For one thing, students who did not complete all three major assignments could not pass the course. Many struggled with technology, especially in the final assignment, in which they were asked to create a video presentation based on a personal philosophy or belief. Some students, for privacy and cultural reasons, chose not to complete that assignment, even when we changed the guidelines to require only an audio presentation with visual elements. There were other students who joined the course after the second week; we cautioned them that they would not be able to pass it because there was no mechanism for doing peer review after an assignment’s due date had passed.
These results seem consistent with Mike Hewner’s thesis results. If a student likes her intro course more, they are more likely to take that major. Students use how much they enjoy the course as a proxy for their affinity for the subject.
Undergraduates are significantly more likely to major in a field if they have an inspiring and caring faculty member in their introduction to the field. And they are equally likely to write off a field based on a single negative experience with a professor.
Those are the findings of a paper presented here during a session at the annual meeting of the American Sociological Association by Christopher G. Takacs, a graduate student in sociology at the University of Chicago, and Daniel F. Chambliss, a professor of sociology at Hamilton College. The paper is one part of How College Works, their forthcoming book from Harvard University Press.
I couldn’t believe this when Mark Miller sent the below to me. “Maybe it’s true in aggregate, but I’m sure it’s not true at Georgia Tech.” I checked. And yes, it has *declined*. In 2003 (summing Fall/Winter/Spring), the College of Computing had 367 graduates. In 2012, we had 217. Enrollments are up, but completions are down.
What does this mean for the argument that we have a labor shortage in computer science, so we need to introduce computing earlier (in K-12) to get more people into computing? We have more people in computing (enrolled) today, and we’re producing fewer graduates. Maybe our real problem is the productivity at the college level?
I shared these data with Rick Adrion, and he pointed out that degree output necessarily lags enrollment by 4-6 years. Yes, 2012 is at a high for enrollment, but the students who graduated in 2012 came into school in 2008 or 2007, when we were still “flatlined.” We’ll have to watch to see if output rises over the next few years.
Computer-related degree output at U.S. universities and colleges flatlined from 2006 to 2009 and have steadily increased in the years since. But the fact remains: Total degree production (associate’s and above) was lower by almost 14,000 degrees in 2012 than in 2003. The biggest overall decreases came in three programs — computer science, computer and information sciences, general, and computer and information sciences and support services, other.
This might reflect the surge in certifications and employer training programs, or the fact that some programmers can get jobs (or work independently) without a degree or formal training because their skills are in-demand.
Of the 15 metros with the most computer and IT degrees in 2012, 10 saw decreases from their 2003 totals. That includes New York City (a 52% drop), San Francisco (55%), Atlanta (33%), Miami (32%), and Los Angeles (31%).
This is our problem in computing, too. If students have never seen a computer science course before coming to college, they won’t know what hits them when they walk in the door.
Experts estimate that less than 40 percent of students who enter college as STEM majors actually wind up earning a degree in science, technology, engineering or math.
Those who don’t make it to the finish line typically change course early on. Just ask Mallory Hytes Hagan, better known as Miss America 2013.
Hagan enrolled at Auburn University as a biomedical science major, but transferred to the Fashion Institute of Technology a year later to pursue a career in cosmetics and fragrance marketing.
“I found out I wasn’t as prepared as I should be,” Hagan said during a panel discussion today at the 2013 U.S. News STEM Solutions conference in Austin. “I hit that first chem lab and thought, ‘Whoa. What’s going on?’”
Google has found that being great at puzzles doesn’t lead to being a good employee. They also found that GPA’s aren’t good predictors either.
Nathan Ensmenger could have told them that. His history The Computer Boys Take Over shows how the relationship between academic mathematics and brainteasers with computer science hiring was mostly an accident. Human resources people were desperate to find more programmers. They used brainteasers and mathematics to filter candidates because that’s what the people who started in computing were good at. Several studies found that those brainteasers and math problems were good predictors of success in academic CS classes — but they didn’t predict success at being a programmer!
How many people have been flunked out of computer science because they couldn’t pass Calculus — and yet knowing calculus doesn’t help with being a programmer at all?!?
You can stop counting how many golfballs will fit in a schoolbus now. Our Favorite Charts of 2013 So FarBen Bernanke Freaked Out Global MarketsGoogle has admitted that the headscratching questions it once used to quiz job applicants (How many piano tuners are there in the entire world? Why are manhole covers round?) were utterly useless as a predictor of who will be a good employee.”We found that brainteasers are a complete waste of time,” Laszlo Bock, senior vice president of people operations at Google, told the New York Times. “They don’t predict anything. They serve primarily to make the interviewer feel smart.”
The growth of departments in the Taulbee report is astonishing, but what Computerworld got wrong is calling it “computer science enrollments,” as opposed to “computer science enrollments in PhD-granting institutions.” The Taulbee report doesn’t cover all CS departments, and that’s why the new NDC survey has been launched.
The Taulbee report also indicates that the percent of women graduating with a Bachelors in CS has risen slightly, while the Computer Engineering percentage has dropped. Both are well south of 15%, though — a depressingly small percentage.
The number of new undergraduate computing majors in U.S. computer science departments increased more than 29% last year, a pace called “astonishing” by the Computing Research Association.
The increase was the fifth straight annual computer science enrollment gain, according to the CRA’s annual surveyof computer science departments at Ph.D.-granting institutions.
In the context of David Notkin’s receipt of the 2013 Computing Research Association A. Nico Habermann Award for outstanding contributions to supporting underrepresented groups in the computing research community, Lecia Barker of the National Center for Women & Information Technology (we hosted their Washington State Awards for Aspirations in Computing last weekend) sent us the chart to the right, comparing UW CSE’s performance to the national average in granting bachelors degrees to women.
It was really great to see these results in the U. Washington CSE News, but it got me to wondering: Did all the big R1 institutions rise like this, or was this unusual at UW? I decided to generate the GT data, too.
I went to the GT Self-Service Institutional Research page and downloaded the degrees granted by college and gender in each of 2005, 2006, and on up to 2011. (All separate spreadsheets.) I added up Fall, Spring, and Summer graduates for each year, and computed the female percentage. Here’s all three data sets graphed. While GT hasn’t risen as dramatically as UW in the last two years (so UW really has done something remarkable!), but GT’s rise from 2005 far below the national average to above the national average in 2009 is quite interesting.
Why is UW having such great results? Ed Lazowska claimed at SIGCSE 2013 that it’s because they have only a single course sequence (“one course does fit all,” he insisted) and because they have a large number of female TAs. I don’t believe that. I predict that more courses would attract more students (see the “alternative paths” recommendation from Margolis and Fisher), and that female TA’s support retention, not recruitment. I suspect that UW’s better results have more to do with the fact that GT’s students declare their major on their application form, while UW students have to apply to enter the CSE program. Thus, (a) UW has the chance to attract students on-campus and (b) they have more applications than slots, so they can tune their acceptances to get the demographics that they value.
The last paragraph of this is interesting. Yes, Engineering and Computer Science (in particular) are booming, but not everywhere, and it’s not evident to everyone. I was just at Tufts on Monday, where some Engineering students were asking me if Computer Science was growing in enrollment anywhere. Well, there’s Stanford…
Now? According to three stats buried in a press release from the university’s engineering school, Computer Science is the most popular major at Stanford. More students are enrolled in it than ever before (even more than at the dot-com boom’s height in 2000-2001). And more than 90 % of Stanford undergrads take a computer science course before they graduate.
Stanford is Stanford, and its stats aren’t necessarily indicative of academia at large: Countrywide, the most popular major is business. But the school’s computer-heavy numbers reflect its existence, both as a member of what candid college administrators call the Big Four (the other three are Princeton, Harvard and Yale), and as a school nestled close to Silicon Valley’s elite.
In a lengthy feature from earlier this year, the New Yorker’s Ken Auletta revealed that, even beyond Stanford’s CS department, “A quarter of all undergraduates and more than 50% of graduate students [at Stanford] are engineering majors. At Harvard, the figures are 4 and 10%; at Yale, they’re 5 and 8%.”
Mike Hewner successfully passed his PhD dissertation defense on Friday. There are just some dissertation tweaks and bureaucracy to go. In the process of the defense, there were several really interesting implications for his theory that got spelled out, and they relate to some of the comments made in response to my post on his dissertation last week.
Early choice is not early decision: In response to a question about when students should decide their specializations (should it be earlier in the degree or later in the degree), Mike said, “Making a choice early doesn’t force making a decision early.” We then spent some time unpacking that.
In Mike’s theory, students spend time exploring until they face a differential in enjoyment between classes that students interpret as an affinity for one topic over another. Students use this process to decide on a major, or to decide on a specialization area within a major. Once they’ve made a decision, they are more committed, and are willing to go through less-enjoyable classes in pursuit of a goal that they have now decided on. Forcing students to make a choice early (between majors or specializations) doesn’t change this process — they don’t decide earlier to become committed to a major or specialization. Forcing the choice early may mean dealing graduation, when students finally decide on something else and become committed to that other path.
Job as ill-defined goal: One of the surprising and somewhat contradictory ideas in Mike’s thesis is that, while US students today may be more driven to get a college education in order to get a better job or a middle class lifestyle, they don’t necessarily know what that job entails. Students that Mike interviewed rarely could describe what kind of job they wanted, or if they did, it was vague (“Work for Google”) and the students couldn’t explain what that job would require or what classes they should take to prepare for that job.
When we were first developing Threads, we talked about helping students to describe the kind of job they wanted, and then we could advise them to pick the Threads that would help them achieve that career. But Mike’s theory says that that’s backwards. Students don’t know what kind of job they want. They use experiences in the classes to help them decide what kind of work they will enjoy.
Hewner’s theory is constructivist. Mike was asked, “How would you advise a student such that they could figure out the best Thread for themselves?” Mike’s response was that students would need to do something that was authentic and representative of work within that Thread — which is hard to do in an accessible manner for students who don’t know much about that Thread yet. You can’t just tell students about the Threads or about the jobs that fit into the Threads. It’s unlikely that students will be able to successfully predict if they would enjoy the work in the Thread based on a description.
In some sense, Mike’s theory is intensely constructivist. Mike’s students won’t decide on a major, specialization or career choice until they experience the work of that major, specialization, or career choice, and then decide if they enjoy it or not for themselves. If decisions are made based on enjoyment, you can’t tell someone that they’d enjoy the experience. They have to figure it out for themselves.
Interesting new initiative between the White House and NSF to increase the number of graduates in computing and engineering by focusing on retention. (I strongly agree, because retention is where we’ve been focusing our attention.)
This letter announces a cooperative activity between NSF and members of the Jobs Councils High Tech Education working group, led by Intel and GE, to stimulate comprehensive action at universities and colleges to help increase the annual number of new B.S. graduates in engineering and computer science by 10,000. Proposals for support of projects would be submitted under a special funding focus Graduate 10K+ within the NSF Science, Technology, Engineering, and Mathematics Talent Expansion Program STEP, see http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=5488.
Studies have shown that retention during the critical first two years in a students major, or along the path towards declaration of a major, is an excellent predictor of eventual graduation with a STEM degree. Recognizing that the correlation between retention and graduation is particularly strong for students in engineering and computer science, we invite proposals from institutions that can demonstrate their commitment to:(i) significant improvement in first and second year retention rates in these particular majors, beyond current levels; and (ii) sustained, institutionally-embraced practices e.g. http://www.asee.org/retention-project that lead, ultimately, to increased graduation. Jobs Council members anticipate providing support for this special funding focus, with the number of awards to be made contingent on the availability of funds.