Archive for August, 2013
Call for Papers: Special Issue of CS Ed for Doctoral Research
CALL FOR PAPERS
THE JOURNAL OF COMPUTER SCIENCE EDUCATION
SPECIAL ISSUE OF COMPUTER SCIENCE EDUCATION:
DOCTORAL RESEARCH IN COMPUTING EDUCATION
JUNE 2014
Guest Editor:
Allison Elliott Tew, University of Washington Tacoma
Submission Deadline: November 15, 2013
Journal Information: http://www.informaworld.com/cse
Research undertaken in pursuit of a doctorate in any discipline is
important because it is the first work of the next generation of
researchers and highlights the most promising directions for the field.
Doctoral research in our field is particularly interesting because the
researchers have a unique perspective. Much of the research in computer
science education is undertaken by teaching-active faculty. Doctoral
students have the opportunity to reflect on our field from the
perspective of studying the teaching and learning practice, often apart
from being the actual teacher or learner. Thus, doctoral research offers
a compelling lens from which to view the computing discipline.
The guest editor invites authors to submit manuscripts for a special
issue devoted to doctoral dissertation research on areas pertaining to
computer science education. This issue will feature doctoral research
that reports novel contributions to the field of computer science
education and meets high academic standards. Manuscripts should include
a description of the problem, appropriate background research, method of
data collection and analyses, findings, open problems, and future
research agenda.
Full papers will feature doctoral work completed within the past two
years. It is anticipated that these articles will be on the order of
5,000 to 8,000 words. Shorter articles, between 2,000 and 4,000 words,
introducing well developed doctoral research that is not yet completed
can also be published.
Total length of the manuscript must be indicated, including word number
equivalents for illustrations, tables, figures, and charts. Papers must
contain an abstract summary of about 150 words, briefly summarizing the
essential contents.
Prospective authors are invited to send email indicating their intention
to submit a manuscript to the guest editor of the special issue, Allison
Elliott Tew (aetew@u.washington.edu). General questions may also be sent
to The Journal of Computer Science Education editors, Sally Fincher or
Laurie Murphy (CSE.Editors@gmail.com).
Submit manuscripts electronically to:
Allison Elliott Tew
Institute of Technology
University of Washington Tacoma
1900 Commerce Street
Tacoma, WA 98402
Phone: 253-692-4700
aetew@u.washington.edu
Important Dates:
November 15, 2013 : Manuscripts due
January 15, 2014 : Author notification
March 1, 2014 : Final submission due
June 2014 : Publication
The Two Cultures of Educational Reform – NYTimes.com
(Shoot — I meant to put this on “draft” and come back to it, but hit the wrong button. Sigh.)
Here’s what I thought was interesting about this piece: I agree with Fish’s depiction of “data and experiment culture” about education, and the “ineffable culture,” too. But his alignment of MOOCs with “data and experiment culture” of MOOCs seems wrong. Our data about MOOCs says that they’re not working. So, belief in MOOCs is “ineffable.” It’s about having warm feelings for technology and the hopes for its role in education.
About halfway through his magisterial study “Higher Education in America,” Derek Bok, twice president of Harvard, identifies what he calls the “two different cultures” of educational reform. The first “is an evidence-based approach to education … rooted in the belief that one can best advance teaching and learning by measuring student progress and testing experimental efforts to increase it.” The second “rests on a conviction that effective teaching is an art which one can improve over time through personal experience and intuition without any need for data-driven reforms imposed from above.”
Bok is obviously a member of the data and experiment culture, which makes him cautiously sympathetic to developments in online teaching, including the recent explosion of MOOCs (massive open online courses). But at the same time, he is acutely aware of the limits of what can be tested, measured and assessed, and at crucial moments in his analysis that awareness pushes him in the direction of the other, “ineffable” culture.
Defining an alternative to MOOCs with a better chance of engaging women
NYTimes just had a nice article about the Georgia Tech online Masters degree program based in MOOCs. I’m glad that the OMS (Online MS) group is getting that kind of attention.
For my research interests, I’m more excited about the alternative to MOOCs described below. I am not well-versed in feminist perspectives, but I appreciate the values that are informing Anne Balsamo’s design and do see that this approach has a greater chance of drawing in women (based on research like Joanne Cohoon’s) than traditional MOOCs.
At participating colleges, professors will base their own courses on each weekly theme, sharing course materials and assignments, but customizing them for their own students. The courses will vary, as some are undergraduate and some are graduate, and the institutions see list at right vary widely by mission and geography — including institutions in Australia, Britain, Canada and the United States. The class sizes will be between 15 and 30 students each, decidedly non-massive. “There is another pedagogical commitment here,” Balsamo said. “Who you learn with is as important as what you learn. Learning is a relationship, not just something that can be measured by outcomes or formal metrics.”
via Feminist professors create an alternative to MOOCs | Inside Higher Ed.
Trip Report on ICER 2013: Big Data, compare-and-contrast, and middle school girls
The papers for ICER 2013 are available in the ACM Digital Library now at http://dl.acm.org/citation.cfm?id=2493394. I think that they remain free for a month (so, until September 12), so grab them quick.
ICER 2013 was a fabulous conference. I learned alot, and am already using some of the ideas I gained there in my research and in my teaching. I can’t possibly summarize all the papers, so here’s my unofficial list of what struck me.
I was invited to be a discussant in the Doctoral Consortium, and that was an absolute thrill. The students were so bright and had so many interesting ideas. I’m eager to hear about many of the results. We also noted that we had participants from several major research universities this year (Stanford, MIT, Virginia Tech, University of Washington). For some, it was the first time that they’d ever sent someone to the ICER DC. Why? Amy Ko (U. Washington) said that it was because it’s been three years since CE21 funding started, and that’s enough time to have something for a doctoral student to want to show. Really shows the importance of having funding in an area.
One of the big ideas for me at ICER this year was the value of big data — what can you do with lots of data? Neil Brown showed that the Computing At Schools website is growing enormously fast, and he told us that the BlueJ Blackbox data are now available to researchers. Elena Glassman talked about how to use and visualize student activity to support finding different paths to a solution. Colleen Lewis presented with two of her undergraduate collaborators from Berkeley on data mining the AP CS exam answers.
My favorite example of the value of big data for CS Ed came from my favorite paper of the conference. Michael Lee and Amy Ko presented on their research on how adding assessments into a programming video game increased persistence in the game. The below graph appears in their paper, but in the talk, Michael annotated it with what was being taught in the levels that led to drop-offs in participation. (Thanks to Michael for providing it to me.) The control and assessment groups split on lists. Variables were another big drop-off, as were objects and functions. Here is empirical measurement of “how hard is that topic.” I’ve submitted my request to gain access to the Blackbox, because I’m starting to understand what questions we can ask with a bunch of anonymized data.
There were several papers that looked at student artifacts as a proxy for their understanding. I was concerned about that practice. As Scott Klemmer told us in his opening keynote, people program mostly today by grabbing stuff off the Web and copying it — sometimes, without understanding it. Can you really trust that students using some code means that they understand the idea behind that code?
Raymond Lister led a really great one hour special session around the idea of “Geek genes,” whether CS really does generate a bi-modal distribution of grades, and whether the learning edge momentum theory describes our results. It was a great session because it played to ICER’s strengths, e.g., really intense discussion, and yet generated moments of enormous laughter. I came away thinking that there are no geek genes, we don’t have bimodal distributions, and the jury is still out on the learning edge momentum.
Elizabeth Patitsas presented a nice paper comparing introducing algorithms serially (“Here’s algorithm A that solves that problem…and now here’s algorithm B…”) vs as compare-and-contrast (“Here are two algorithms that solve that problem…”). Compare-and-contrast is better, and better when learning algorithms than even the existing education literature suggests. I mentioned this result in class just yesterday. I’m teaching our TA preparation class, and a student who teaches algorithms asked me, “Am I responsible for my students’ learning?” I showed the students Elizabeth’s result then asked, “If you know that teaching one way leads to more learning than another, aren’t you morally or ethically required to teach using the better method?”
Michelle Friend and Rob Cutler described a group of middle school girls figuring out a complicated algorithm problem (involving finding the maximum height that an egg drop protection mechanism will work). They showed that, without scaffolding, the girls were able to come up with some fairly sophisticated algorithms and good analyses of the speed of their algorithms. We’re getting somewhere with our understanding of CS learning at the schools age.
And I totally admit that my impression of this ICER is influenced by my paper on Media Computation winning the Chair’s Paper Award. Michael Lee won the popular vote “John Henry Award.” (I voted for him, too.)
I’m skipping a lot: Mike Hewner presenting on his thesis, an interesting replication of the McCracken study, new ideas about PCK and threshold concepts. It was a great event, and I could write a half dozen posts about the ideas from the conference. Next year’s ICER is in Glasgow, 11-12 August. I am very much looking forward to it, and am working on my papers to submit already.
CSTA Blog on new UChicago study of US CS high school teachers
The University of Chicago has released their latest study on the state of CS Education in US high schools. This one is a survey of CS teachers around the country, and Baker Franke introduces the study on the CSTA Blog.
Two things stood out to me when I looked at survey results. First, computer science teachers, despite still reporting that they are the only CS teacher (or one of a few) in their community, reported feeling supported by their schools and administration. This was completely surprising to me. (Perhaps, it is the self-selecting nature of survey respondents, who are more likely to feel happy, satisfied and proud of the fact that they teach CS.) But, maybe this is evidence that the advocacy work of CSTA has been working and the shifting public view of computer science education has led to more schools supporting the teaching of computer science.
Second, we have a real problem with misconceptions about computer science, still, in 2013. And as the survey results show, as a community, we are still not on the same page about what computer science education is either. At the moment, the word “code” is gaining attention as the stuff students should learn with computers, and whatever stigma used to be attached to programming seems to be dissipating – which is good. But we have a long way to go in clarifying what a high-quality, rigorous computer science education is and that that includes more that just programming.
via Computer Science Teachers Association Blog “Who is Teaching CS?”.
The BlueJ Blackbox now available: large scale programming education data collection
Neil Brown announced this at ICER last week. The new version of BlueJ now anonymously logs user actions onto a server for analysis by researchers. I just signed up to get access to the site. I have a couple of ideas for research projects using these data. It’s pretty exciting: Big data comes to computing education research!
We have begun a data collection project, called Blackbox, to record the actions of BlueJ users. We’re inviting all the BlueJ users (with the latest version, 3.1.0, onwards) to take part. About 2 months in to the project, we already have 25,000 users who have agreed to take part, with 1,000 sending us data each day. Based on current estimates, I expect that in November 2013 we should see around 5,000 users sending data each day, with a total of over 100,000 users. Rather than hoarding the data, we are making this data available to other computing education researchers for use in their own research, so that we can all benefit from this project.
via Blackbox: large scale programming education data collection | Academic Computing.
Programmers insist: “Everybody” does not need to learn to code
I answered the criticism leveled below previously — it really is the case that many people who aren’t professional programmers are going to need to learn to program as part of their other-than-software jobs. Why are programmers pushing back against people learning to code? (And there seems to be a lot of pushback going on, as this mashup suggests.) Is it a sense of “What I do is important, and if everyone can do it, it lessens the importance”? I don’t really think that they’re afraid for their jobs — it does take a lot of hours and effort to learn to code well.
The argument that it won’t “stick” (as suggested below) doesn’t work for me. Just because we don’t know now how to teach computer science to everyone doesn’t mean that we can’t learn how to teach computer science to everyone who needs it. Our lack of ability is not the same as the lack of need. We don’t teach everyone to read well and understand mathematics yet — does that mean we shouldn’t try?
But if you aren’t dreaming of becoming a programmer—and therefore planning to embark on a lengthy course of study, whether self-directed or formal—I can’t endorse learning to code. Yes, it is a creative endeavor. At its base, it’s problem-solving, and the rewards for exposing holes in your thinking and discovering elegant solutions are awesome. I really think that some programs are beautiful. But I don’t think that most who “learn to code” will end up learning anything that sticks. One common argument for promoting programming to novices is that technology’s unprecedented pervasiveness in our lives demands that we understand the nitty-gritty details. But the fact is that no matter how pervasive a technology is, we don’t need to understand how it works—our society divides its labor so that everyone can use things without going to the trouble of making them. To justify everyone learning about programming, you would need to show that most jobs will actually require this. But instead all I see are vague predictions that the growth in “IT jobs” means that we must either “program or be programmed” and that a few rich companies want more programmers—which is not terribly persuasive.
via “Everybody” does not need to learn to code. – Slate Magazine.
I saw the below exchange on Twitter, and thought it captured the argument well:
Why we need schools of education for a stable future for computing ed
I’ve mentioned before how much we need schools of education to guarantee the future stability of computing education. The new CSTA report on certification makes the point better than I do.
I just wrote a Blog@CACM post explaining why we in CS need collaboration with schools of education. We don’t want to be in the business of certifying teachers. We certainly do not have the background to prepare teachers for a lifelong career in education. That’s what pre-service education faculty do.
How we get from here to there is an interesting question. Michelle Friend suggests that we start by finding (or getting hired) faculty in science and mathematics education who are interested in starting computing programs. Few schools would be willing to take the risk of establishing computing education programs or departments today. They might exist one day, but they’ll probably grow out of math or science ed — just as many CS departments grew out of math or science or engineering roots.
Given that (in the US) we lose close to 50% of our STEM teachers within the first five years of teaching, we have to establish reliable production of CS teachers, if we don’t want CS10K to be only CS5K five years later. To establish that reliable production, we need schools of education.
New CSTA Report released on (sad) state of CS teacher certification in US
Copied from the press release sent to me by Stacey Finkel, CSTA’s media person. I’ve just started reading the report, and it’s really interesting.
A new report released today by the Computer Science Teachers Association (CSTA) finds that Computer Science teacher certification/licensure in the United States is deeply flawed. While companies look to fill lucrative positions in the computing field, there is a critical shortage of qualified teachers to teach the next generation of computer scientists.
Bugs in the System: Computer Science Teacher Certification in the U.S., developed with support from Google, is a comprehensive study of all 50 states and the District of Columbia revealing that each state (and in some states each school district) has its own process, its own definition of Computer Science, and its own idea of where Computer Science fits in the academic program and who is qualified to teach it.
Bugs in the System reveals that only two states (Arizona and Wisconsin) require teachers to be certified/licensed in Computer Science and in many states there are no requirements for teaching Computer Science at all, meaning teachers with little or even no Computer Science knowledge can teach it and teacher preparation institutions are unlikely to offer programs for new Computer Science teachers.
The report also reveals confusion at all levels about what Computer Science is and the knowledge required to teach it. As a result, teachers who want to teach Computer Science can be faced with, sometimes insurmountable challenges. For example, in Florida becoming a certified Computer Science teacher requires taking a course called K-6 Computer Science Methods, however the course is not offered in any teacher preparation program in the state.
A portable graphics library for introductory CS: Cross-platform MediaComp!
I just bumped into this paper looking for something else — how cool! Eric Roberts and Keith Schwarz have created a cross-platform layer on top of a Java server process, so that their portable graphics library (which includes facilities for doing pixel-level manipulations, as we do in MediaComp) can be accessed from anything! I often get asked “How can I do MediaComp in C++?” Here’s a way!
For several decades, instructors who focus on introductory computer science courses have recognized the value of graphical examples. Supporting a graphics library that is appropriate for beginning students has become more difficult over time. This paper describes a new approach to building a graphics library that allows for multiple source languages and a wide range of target architectures and platforms. The key to this approach is using an interprocess pipe to communicate between a platform independent client library and a Java based process to perform the graphical operations specific to each platform.
Virtual Faculty Communities of Practice to improve instructional practices
Posted to the SIGCSE-Members list — I really like this idea! Our work on DCCE showed that communities of teachers was an effective way of improving teacher’s sense of belonging and desire to improve. Will it work for faculty? ASEE is the organization to try!
This is a great opportunity for CS faculty to work with like-minded faculty from across the country to explore and share support for introducing new instructional practices into your classroom. Please consider this for yourself and pass it on to your colleagues.
Engineering education research has shown that many research-based instructional approaches improve student learning but these have not diffused widely. This is because (1) faculty members find it difficult to acquire the required knowledge and skills by themselves and (2) sustaining the on-going implementation efforts without continued encouragement and support is challenging. This project will explore ways to overcome both obstacles through virtual communities.
CS/IT higher-ed degree production has declined since 2003
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%).
via In the Spotlight: Higher Ed Degree Output by Field and Metro | Newgeography.com.
Computer Scientists: do outreach or your science dies
All the more reason for more computer scientists to answer Cameron Wilson’s “All Hands on Deck!” call, and to get involved in the CS10K effort.
The scientific community must also do the same, by convincing the public that it is worth spending tax dollars on research. Scientists: this isn’t someone else’s job – this is your job, starting immediately. If you personally hope to receive government research funds in the future, public engagement is now part of your job description. And if you and your colleagues don’t convincingly make the case to the public that your discipline should be funded, well then it won’t be. Without a public broadly supportive of funding science, it is all too easy for politicians looking for programs to cut to single out esoteric-sounding research programs as an excuse to further slash science funding.
via Scientists: do outreach or your science dies | Guest Blog, Scientific American Blog Network.
“Woman (music) composer” is even more rare than “women programmer”
In September, I’m going to Dagstuhl for a workshop on “Collaboration and Learning through Livecoding.” It’s mostly people who “livecode” in the musical sense and put on “AlgoRaves“, but will also include people who talk about “livecoding” in the CS education sense. I’m really looking forward to it.
But I was surprised when I looked at the list of attendees: There is only one woman that I can identify by name. I figured that music would be a more welcoming community than computer science, so there would be lots of women coming to livecoding from the music side. I asked my colleague, Jason Freeman, in GT’s School of Music, who told me that music composition is even more male than computer science! He told me about the NYTimes piece (linked below) that talks about the severe gender disparity in music composition. Jason said that GT’s Masters in Music Technology program has had 60-70 graduates so far — and only six women cumulatively (across all years).
Why is that? What’s common about music composition and computer programming that both careers would have so few women, when fields like medicine, law, and veterinary science used to be male-dominated but are now more gender-balanced?
Kirsten acknowledged that women have had an agonizingly difficult time gaining a creative foothold in classical music, whose repertory is male-dominated to a stifling degree. But, in light of the international renown of such figures as Kaija Saariaho, Unsuk Chin, and Sofia Gubaidulina, she argued that the “woman composer” no longer required special pleading or affirmative action. “Neither art nor artist is served by segregation—even if it’s well intended,” Kirsten wrote. Rather than going out of their way to boost female composers, she suggested, programmers should embrace only works that speak to them strongly, trusting that women will continue to advance.
via Alex Ross: Female Composers Edge Forward : The New Yorker.
Experiences with Media Computation at U. Adelaide
Katrina Falkner has written up an excellent reflection (with gorgeous example student work) on her new MediaComp course at the University of Adelaide. I loved the artwork she shared, and I was particularly struck by the points she made about the value of “slowness” of the language, the challenges of helping students decontextualize programming after learning MediaComp, and the students complaining about using a curriculum “not invented here.”
The students didn’t really like working with Jython as it was very slow, but this had an unintended consequence, in that they became aware of the efficiency of their algorithms. I don’t think I have ever taught a first year course where students introduced efficiency as a discussion point on their own initiative. However, when working with their own images, which could sometimes be huge, they had to start thinking about whether there was a better way of solving their problems. I think this was a big win.
Creativity and ownership. The last assignment we ran was a group assignment, where the students had to develop a piece of art using and extending the techniques they had learnt in the course. This was fantastic. We had run a similar assignment in previous years where the students developed JavaScript games, and that worked reasonably well, but I think Media Computation produced a better result as the outcomes were more individual, and creative. The students had a lot more fun sharing their results.
Computing Ed Research - Guzdial's Take 
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