Archive for March, 2010
Recursion by Pirolli (1991)
I heard Greg Wilson’s request for me to talk about the papers I’m reading (especially since I owe him a chapter which is a review of literature), so I thought I’d talk about one that Barb and I have been thinking a lot about lately: Effects of Examples and Their Explanations in a Lesson on Recursion: A Production System Analysis by Peter Pirolli (1991), in Cognition and Instruction, 8(3), 207-259. In this paper, Pirolli describes two studies where he explores what kind of examples are useful in learning to write recursive functions, and how the characteristics of the example influences what errors students make when they write their own recursive functions. It’s a dense paper, with some sophisticated quantitative analysis on error rates.
I was interested in this paper as one of the first in computer science to build upon Sweller’s worked examples research. Pirolli was explicitly trying to understand the role of examples in problem-solving and about usefulness of different kinds of examples. The first interesting tidbit that I got from this paper is how many examples Pirolli sees as necessary to learn the basics of the language. He’s teaching students to write recursive functions with a version of Lisp (called “SIMPLE”) with only 7 primitives. Here’s an example SIMPLE program:
During the training phase, when he’s just bringing people up to speed on these primitives, he provides 8 examples for each of the 7 primitives. 56 individual examples (where he shows the primitive applied to a list, the student is asked to guess the result, and if fails, the system provides the result) is a lot of examples just to get students familiar with the language. When you teach CS1, do you show 8 examples of for loops before students try to use them in an assignment?
The most interesting lesson I learned about recursive examples from this paper comes from the two conditions in his first experiment. In one condition, the recursion examples that students work through are about how to write a recursive function (e.g., “here’s the base case” and “here’s how it recurses”). In the other condition, the recursion examples are about the dynamics of how it works (e.g., “first there’s this call, then the same function is called but with this input, then the result is returned…”), like this:
Here’s the bottomline of what he finds: Getting students through the “how to write” examples took on average 57 minutes, while the “how it works” examples took an average of 85 minutes. There was no statistical difference in performance on a post-test on writing recursive functions, though the “how to write” group had slightly fewer errors.
Even more intriguing is the discussion where Pirolli relates these findings to others in John Anderson’s group at the time which suggest, “that knowledge about how recursive functions are written is different from knowledge about how they work” and “that there is little transfer of how-it-works knowledge to function-writing tasks and, more interestingly, that extensive additional practice with simulating the evaluation of programs yields no significant benefit in debugging tasks when compared with extensive practice just coding programs.” Writing code and tracing code are completely different tasks.
Barb is helping to teach an AP CS class this semester, and she’s teaching recursion right now. She’s basing how she teaches recursion on Pirolli’s results. Her first activities have the students looking at recursive functions and highlighting the base case and the recursive call — just figure out the structure. Then they write some recursive functions. This is Pirolli’s Experiment #1 process, which takes students less time, giving them an early success with less frustration. Next, she’ll get into debugging recursive functions, which Pirolli suggests is really a different task entirely.
Pirolli’s paper isn’t the definitive statement on teaching recursion or using worked examples. If it was, he wouldn’t have gone on to write several more papers, including several with his students at Berkeley on using examples to learn recursion. It is a nice paper that provides good evidence with some practical implications for how we teach.
Congratulations to Matthias!
Matthias Felleisen, innovative educator and developer known for the TeachScheme approach, has won the 2009 ACM Karlstrom Award.
Matthias Felleisen, recipient of the Karl V. Karlstrom Outstanding Educator Award for his visionary and long-standing contributions to K-12 outreach programs. In 1995, he founded the TeachScheme! project, which has trained over 700 educators; he was also instrumental in setting up the Bootcamp afterschool programs for students in groups that are underrepresented in the computing field. A Trustee Professor at Northeastern University, Felleisen contributed the innovative idea of a design recipe to the computing curriculum, a set of steps that helps students focus on problem solving and logical thinking instead of computer details. The Karlstrom Award recognizes educators who advanced new teaching methodologies; effected new curriculum development in Computer Science and Engineering; or contributed to ACM’s educational mission.
Carl Wieman nominated for White House post
Carl Wieman has been nominated by President Obama for the office of Associate Director for Science in the Office of Science and Technology Policy. Does this mean that he’ll no longer be directing the Carl Wieman Science Education Initiative at UBC?
President Obama Announces More Key Administration Posts, 3/22/10 | The White House.
Responding to the Respondents: Why So Few? Women in STEM and IT
This new AAUW report has come out at nearly the same time as the new report from Caroline Simard at the Anita Borg Institute on Senior Technical Women. We’ve heard the general tune in these reports before, but the nuance and orchestration is different. We’ve heard before that there are biases and cultural barriers that prevent women from succeeding in STEM fields. What’s different is that these reports are responding to the respondents. The AAUW report, for example, responds to a neuroscience-aware world that says male and female brains are different (ala Larry Summers), but shows that biology and “innate ability” alone doesn’t account for the rapid shift away from STEM fields by girls. The ABI folks have told us before that female IT managers lose a lot of time to family responsibilities, but the new report shows that that time doesn’t take away from work time (as has been argued in comments in this blog) — the family time takes away from their social time. The sum of these reports is saying, “Really — it’s the structure and the culture. We’ve considered everything else. The system and the people in it have to change.”
In an era when women are increasingly prominent in medicine, law and business, why are there so few women scientists and engineers? A new research report by AAUW presents compelling evidence that can help to explain this puzzle. Why So Few? Women in Science, Technology, Engineering, and Mathematics presents in-depth yet accessible profiles of eight key research findings that point to environmental and social barriers – including stereotypes, gender bias and the climate of science and engineering departments in colleges and universities – that continue to block women’s participation and progress in science, technology, engineering, and math. The report also includes up to date statistics on girls’ and women’s achievement and participation in these areas and offers new ideas for what each of us can do to more fully open scientific and engineering fields to girls and women.
via Why So Few? Women in Science, Technology, Engineering, and Mathematics | AAUW.
Mellon Foundation Closes Program Funding Sakai: It’s not about PowerPoint
The Andrew W. Mellon Foundation is closing a grant program that financed a series of high-profile university software projects, leaving some worried about a vacuum of support for open-source ventures.
Mellon’s decade-old Research in Information Technology program, or RIT, helped bankroll a catalog of freely available software that includes Sakai, a course-management system used by Stanford University and the University of Michigan; Kuali, a financial-management program recently rolled out at Colorado State University; and Zotero, a program for managing research sources used by millions.
This news got me thinking about something completely different from Sakai. I went to one of the early meetings when the Mellon Foundation was forming these coalitions. There was a lot of excitement about universities working together to create open source software to solve important educational problems. At the meeting that I attended, a number of wish-lists were generated: What should future educational software include? Then these lists were sorted into what absolutely, positively had to be there, what would be useful to have, and what was unlikely to happen. What surprised me at the time was how much was on that “absolutely, positively” list. Some of the items didn’t seem so absolute-positive to me, like image databases (for fields like mechanical engineering) that could search based on similar images (e.g., “Here’s a picture of a gear. Where do gears of this pitch and size show up in other pictures?”).
One of those items on the absolutely-positively list was called “PedaPoint.” The idea was to create a kind of PowerPoint that enforced what we know about good Pedagogy (“Pedagogical Powerpoint” => “PedaPoint”). At the time, I thought that that was an outlandish goal. Today, in reflecting back on the Mellon Foundation’s ending of this research program, I realize that it was also the wrong goal.
I’ve been reading more of the literature to which Carl Wieman pointed in his SIGCSE talk. It’s not rocket science, looking at it from the cognitive/learning sciences perspective. It’s totally obvious considering it from what we know about learning. And yet, as Carl pointed out, we don’t teach correctly from a scientific perspective. It’s not what we say, it’s what the students do. Getting the students to think, getting the students to argue, getting the students to make decisions, and get those decisions corrected if they’re wrong — that’s where the learning comes from. You can’t make learning work much better from fixing PowerPoint so that a teacher only says the right things. (Of course, you can make learning work better by changing PowerPoint so that it’s more about what the students do!)
Sakai probably helped student learning more than a PedaPoint might have. We use Sakai here at Georgia Tech, and there are lots of people worried now that Mellon support has faded. Still, Sakai is a learning management system, and while that’s the “standard” for online courses, I do hope that we can do better.
Need help! Get CS in the Common Core!
Cameron Wilson just wrote a blog post about computer science being made part of the draft Common Core Standards. It’s not yet in the official Common Core Standards. Cameron explains in his piece why this is important, and what we can do (YES, THERE’S SOMETHING URGENT TO DO HERE!) to make this stick. It can be as simple as an email or filling out a Web form — please do help!
K-12 computer science education might get a boost from a recently released document called the Common Core State Standards Initiative (CCSSI) . This initiative is historic for the United States. For the first time forty-eight governors have come together to propose a common set of English arts and mathematics standards — which are key drivers of the curriculum students are exposed to — for their states. Until the common core standards initiative, state standards were generally disconnected from each other. The exciting news is that computer science is listed as a potential fourth course in their model pathway…
When it comes to computer science education in K-12 we have two major policy issues: 1) most states do not have specific computer science standards, and 2) if computer science courses are in schools, they don’t count toward a student’s core credits. Some states like Texas, Georgia and Virginia have moved to count computer science courses in high school as either a math or science; however, in most states computer science is an elective. This leaves computer science courses starved for attention, resources and student interest.
…Now the community can support this breakthrough by sending letters for support for the inclusion of computer science in the final document. The initiative is taking comments on the draft until April 2. There are two ways to comment. The first is by taking the survey, which as an additional comment area where you can express support for computer science. (Follow this link and click on the “submit feedback” to get to the survey.) The second is by sending letters to commonstandards@ccsso.org.
via Computing and the Common Core | blog@CACM | Communications of the ACM.
The limits of what people generally know
A thread running through my day yesterday (at the NSF CPATH PI’s meeting) and this morning (via email, some at a more outlandish level) is the limits of what people (in general) know (in general).
- Starting from the most outlandish, 24% of all Republicans (according to Harris Polls) believe that Barack Obama may be the Anti-Christ. What’s more interesting (less political, more concerning as educators) is the enormous gap between the educated and less-educated — 43% of Americans with no college education believe that Obama is a Muslim, while only 9% of college educated Americans believe that.
- Moving to the more intellectual and profound, David Brooks has a terrific op-ed piece in this morning’s NYTimes suggesting that economists missed the worldwide meltdown because they relied too much on mathematics, and too little on the morality that early economists relied upon. Brooks points out: “The moral and social yearnings of fully realized human beings are not reducible to universal laws and cannot be studied like physics,” and that earlier economists understood this:
Economics is a “moral science,” Keynes wrote. It deals with “motives, expectations, psychological uncertainties. One has to be constantly on guard against treating the material as constant and homogeneous.”
I am at a meeting of 100 funded investigators in the National Science Foundation (NSF) program in CISE Pathways to Reinvigorate Undergraduate Education (CPATH). These are mostly Computer Science (some IT, some IS) faculty who are working to improve the state of undergraduate computing education.
Yesterday at lunch, someone asked me about the new AP CS effort in which I’m involved. “When is the new AP CS going to be available?” “Around 2015, I expect.” “That’s terrible! That means that there is no AP CS available until then?!?” “No, that’s not true.” “I heard that AP CS was canceled.” Sigh! “No, only the Level AB exam was canceled. The Level A exam is still available, with no plans to get rid of it.” If the belief that “the AP CS is canceled” is still showing up here, among the CS faculty most involved in computing education, what do most CS faculty think?
Yesterday, Cameron Wilson, Director of ACM Public Policy, gave a talk about the effort to make computer science count as a math in the new core curriculum standards. (By the way, I now have the citation for where the Federal Register said that computer science is part of STEM.) There was a fascinating pushback from the PI’s. “But this is replacing one problem with another problem. We don’t want one computing course. We want computing taught pervasively throughout the curriculum.” In talking with people later, I got the sense that the argument was: “We already have computing in the high schools, but it’s only in certain classes. We want it throughout math and science!”
This one is hard to respond to, because I completely agree with the goal. The problem is the base assumption. I don’t think most CS faculty realize how little high school CS is really out there, even among CS faculty who are CPATH PI’s.
Pick a random high school in the United States. With enormous probability, it will have zero computer science. In Georgia, where we have more high schools teaching CS than any state in the Southeast (to the best of our knowledge), the probability is ~70% that a random high school will NOT have computer science. There are over 20,000 high schools in the United States, and only 2,000 AP CS teachers. Those high schools that have a CS teacher typically have a math, science, or (especially in Georgia) business teacher who has had workshop training to teach computer science — or not. There are very few (less than one per state, more like one for every 10 states) classes on how to teach computer science. If you’re not happy with how computer science is now taught in high school, how will you feel about every science teacher (with little or no training) also teaching computer science?
Of course, the correct answer is “Well, get them training!” Agreed! But that’s where we’re at today. Think about Jan Cuny’s challenge in project CS10K: let’s have 10,000 teachers in 10,000 high schools ready to teach AP CS in 2015. That’s going from 2,000 in 2010 to 10,000 in 2015 — and even then, we’ll be in less than half of US high schools. I have little idea how we can ramp up 8,000 more teachers in five years, but that’s a small challenge compared with teaching every science teacher in the country how to teach computer science.
All of the examples in this post point out limitations of what people know, but not limitations of what is known. Most people know that Obama is not Muslim. Economists used to think beyond mathematics. The College Board will be glad to tell you that the AP CS Level A exam is not canceled. And the state of high school computer science is described by CSTA every chance they get. The problem is the distribution of this knowledge, who has it, and who needs it. For all the mass media and Internet news sources and social media that we have, there is still a role for education — helping people to learn what they need, whether or not they recognize that need.
Math Scores are Rising, while Reading is Stagnant
I found these results surprising. I’m sure that computing teachers who read this list will be shocked to hear that their students mathematics skills are rising. What I’m more surprised at is the stagnation of reading skills. While newspaper sales are down, book sales are up, reading on the Internet is what’s killing newspapers, and even texting may have a positive impact on language skills. Given all of that, I would have predicted the reverse — people are generally doing much more with words than with numbers today.
“The nation has done a really good job improving math skills,” said Mark Schneider, a vice president at the American Institutes for Research and a former official at the Education Department, which oversees the test, known as the National Assessment of Educational Progress. “In contrast, we have made only marginal improvements in reading.”
Why math scores have improved so much faster than reading scores is much debated; the federal officials who produce the test say it is intended to identify changes in student achievement over time, not to identify causes.
New CRA Taulbee Report Released: CS Majors up!
The latest CRA (Computing Research Association) Taulbee report is out:
The number of new students majoring in computer science increased 8.5 percent over last year. The total number of majors increased 5.5 percent, yielding a two-year increase of 14 percent. Computer science graduation rates should increase in two to three years as these new students graduate.
via CRA Taulbee Report: CS Enrollments, New Majors Up For 2nd Straight Year.
I have two caveats to mention on this report. First, this is the CRA — research-focused, PhD-granting institutions. I don’t know if it’s the same at the smaller schools, and I don’t know who measures that. From what I’ve heard, things have flattened out at smaller schools, but are not much better.
While these are positive signs, it’s kind of like the signs that the economy is improving. Yeah, it is, but there sure are lots of unemployed people and boarded up stores yet. Look at this graph that I’m copying from the CRA website:
Are the number of new majors increasing? Absolutely! But there is a huge gap between 2000 and 2009. It’s better, but it’s not what it was.
Microsoft is all about cloud computing and parallelism
I don’t know a lot about parallel computing, so there may be no conflict here. Let me explain my confusion. First this:
Microsoft is “all in” for cloud computing, Microsoft CEO Steve Ballmer told a large crowd at the University of Washington’s Paul G. Allen Center for Computer Science & Engineering early this month.
Currently, about 70 percent of Microsoft’s 40,000 employees are “doing things that are entirely cloud-based or cloud-inspired,” he said, adding: “And by a year from now, that [number] will be 90 percent.”
Then this: At the final plenary of SIGCSE 2010, Michael Wrinn of Intel exhorted, cajoled, and insisted that all the educators in the audience teach parallelism to students. For example, he encouraged us to move away from languages like Java and C, towards C#, but even better, toward functional languages like Haskell and OCaml because these can be more easily and more efficiently parallelized. Microsoft and Intel have funded two large research centers (about $10M each, I understand) to improve our ability to program multi-core, parallel computers, at Berkeley and at UIUC.
Here’s where I’m confused: As I understand it, clouds are massive server farms, but each server could be programmed in traditional serial style (even if they are multi-core, which they most certainly will be in the future), right? So is Microsoft hedging its bets, by going “all in” on the Cloud and pushing toward more parallel programming? It’s important for us as educators. To move our curricula to all functional is (for most schools) a big change. Is the future about parallelism, or does the cloud make an emphasis on parallelism less critical, except as an optimization technique to better utilize the multiple cores?
Sally Fincher and women in computing education research for Ada Lovelace Day
I took the pledge to write about a woman in computing that I admire for Ada Lovelace Day. Like last year when I wrote on three local female computer scientists, I had a hard time picking just one. The rules say that you can write about more than one, but the pledge form assumes only one. So I’ll write on one but mention three more, on the theme of women in computing education research.
Sally Fincher is the world’s leading computing education researcher. She leads a computing education group that includes superstars like Ian Utting and Michael Kölling. If we were to list the next top 20 computing education researchers in the world, we would find that Sally has taught (through the Bootstrapping or Scaffolding projects) and/or mentored over a dozen (maybe all 20?) of those. She’s defined research methods that are used all over the world. Perhaps even more significantly than all of that is that she has worked to create the infrastructure for computing education research to grow worldwide.
I first met Sally at the ITICSE 2001 conference that she co-hosted at her home institution, University of Kent at Canterbury. It was at that conference that Mike McCracken organized the first multi-institution, multi-national study in computing education in which I participated. Mike realized that all computing education research projects were going to get hung up on institutional or cultural differences, unless you increased the number of students, institutions, and countries involved. Sally was fascinated by this project and hung out often with the working group. While she didn’t invent MIMN studies, she created several, and wrote the paper that defined how these work and how to design one.
I wrote earlier this month, when Sally won the ACM SIGCSE Outstanding Contribution in CS Education award, about her work with Disciplinary Commons with Josh Tennberg. Suffice to say here, there are additional Commons growing up all over the place now.
Probably her most impressive achievement is her efforts to grow computing education research in the United States (and Australia and elsewhere, but I mostly know the US work). Andy Bernat was at NSF, and he wanted to get computing education research started again. Through Josh Tenenberg, he involved Sally and Marian Petre to literally “bootstrap” computing education research, by running a multi-year workshop to teach fledgling computing education researchers how to do it. Sally went on to run others of these efforts, to create dozens of new researchers.
But after generating all these researchers, Sally realized that her new folks had a problem. Where were they going to publish? Most of the papers they submitted to the SIGCSE conference were rejected, because that really isn’t the place for research papers. So, Sally set out to create the infrastructure to grow an entire research community.
- With Marian, she wrote the guidebook Computer Science Education Research to tell others how to do this.
- With Richard Anderson and me, she created the ACM SIGCSE’s International Computing Education Research (ICER) workshop, which is now in its fifth year.
- She co-edits the Journal of Computer Science Education Research so that there is both a conference and journal venue for her new researchers.
Sally is an amazing person. I can’t imagine someone who has done more to launch a research field than she has. She inspires me a great deal.
I wanted to write about more than one female computing education researcher, despite the pledge restrictions, so let me mention three others briefly.
- Beth Simon is the most energetic and productive researcher that I’ve ever met in any field, and we’re blessed to have her in computing education research. She just got started publishing in this field in 2004, and already has over 30 publications. Among my favorites of her projects is the effort to define “commonsense computing” — what do students know about computing before we get started teaching them?
- Sue Fitzgerald was my co-chair for SIGCSE 2009. Sue’s at a small, urban school with a significant teaching load, and yet she publishes more journal articles than I do (and I’m at a research university with several doctorate students). She does an amazing job of maintaining and leveraging collaborations with a team of researchers around the world. I am impressed with her tenacity and her ability to just keep doing good work.
- Caroline Simard is the head of research for the Anita Borg Institute. She does these studies of women in the workplace that stick in my head and influence my research directions. Her earlier study of female mid-level IT managers has been influencing my thinking about computing education beyond formal school. She’s just completed a new study of female senior-level managers which blows away stereotypes about women in IT. Yes, female managers in IT have family responsibilities, but that doesn’t mean that they put any fewer hours in their jobs — they steal from other time, like having less social time than men. Caroline is doing terrific work that tells us about the realities and the needs in areas where few other researchers are looking.
Michael Kölling
A Post Goes Viral
Last October, I rewrote a blog post that I had created here, on using ideas like reduced cognitive load and worked examples to improve computing education, for Blog@CACM. That post, with Judy Robertson’s rebuttal, appeared in the most recent issue of Communications of the ACM. From there, the post “went viral.”
Yesterday, I was told that that blog post had received 15,000 page views last month — three times what the CACM website home page received. Today I received an email at 9 am: “over 3,761 pageviews on Saturday, 3,711 on Sunday, 10,295 on Monday, and 9,729 today.” I just received an update at just after noon that the post has had 12,304 hits so far today. Why would a six month old post suddenly become so popular? I’ve started getting interesting email connected with the post. A Georgia Tech alum wrote me, saying that his social networking site was discussing the post, and could I stop by to comment in response?
To most of the email and social networking commentary that I’ve seen, my response has been basically the content of the post on hybrid approaches. Of course, students should program during their first course — that’s not all that they should do, and that’s probably not the first kind of practice that they get in computing. Judy’s response is quite nice in describing other kinds of activities that one might use in an introductory course. Unfortunately, I don’t believe that those teaching practices are widespread.
I don’t really understand what causes a spike in interest like this. The CACM website folks are a little concerned about dealing with the “slashdot effect.” While it’s exciting, it’s not quite clear whether to be thrilled or bracing for impact. Just getting a lot of attention is not necessarily a good thing — simply the fact that people are rubbernecking doesn’t mean that there’s something good going on. Perhaps the most interesting part of this is how silent it is. 12,000 people today visited something I wrote, and all I know is that I got a few extra email notes. Works for me!
Requiring isn’t the same as Improving
Here at Georgia Tech, all students are required to take introductory computer science. For the first four years of that policy, we taught the same (single) intro course that we ever did. Our results are similar to what Chicago is finding with its new science requirement.
A policy change that made college-preparatory courses the default high school curriculum in the Chicago public schools increased the number of science courses that students took and passed. But it also kept some students from taking higher-level science courses and did not increase the college-going rate, according to a study by the Consortium on Chicago School Research.
via Education Week: Effect of Chicago’s Tougher Science Policy Mixed.
I particularly liked this quote — taking more of the same thing just leads to more classes in which students do badly:
“Before the policy, most students received C’s and D’s in their classes,” he said. “If they weren’t being successful with one or two years of science, why would we think they would be successful with three years of science, if we don’t pay attention to getting the students engaged?”
Can computing curricula be neutral?
Erik asked a great question in a comment to the “White Boys are Boring” post (a post which was clearly accompanied by a healthy serving of hyperbole, as Kurt pointed out):
Has anyone looked at the comparative efficacies of race/gender neutral programs to increase participation versus ones targeted at specific races or at women?
I do know that curriculum designed to address the needs of women and members of underrepresented minorities work better at attracting those students than the traditional ones — that’s one of the directions that the NSF BPC program has been exploring. That’s not answering Erik’s question, though. The traditional computing curriculum is not neutral.
Media Computation was not designed explicitly to attract women and minority students. We designed Media Computation to attract Liberal Arts, Architecture, and Management majors, and we used sources like Margolis and Fisher’s Unlocking the Clubhouse to inform our decisions. The result is that no published study has found a difference in success rates due to gender or ethnicity, and the published studies show that women are more likely to succeed with Media Computation than with whatever was the traditional curriculum. That doesn’t mean that Media Computation is neutral — some students dislike it. The distinction doesn’t seem to be due to gender or ethnicity.
When we design computing curricula, most teachers aim to make assignments and examples motivating and interesting, and in so doing, we speak to some members of our audience, and not others. When we use video games or robots in examples, for example, we tend to get the boys more engaged than the girls. I’ve found that it’s hard to be culturally neutral in my own assignments. One year, I used an example in an object-oriented design course about parts of a car (lots of opportunity for aggregation and part-of relationships there), only to find that my students from the developing world didn’t have much experience with cars and didn’t know anything about parts of an engine. Our introductory courses used to build assignments around board games like Yahtzee and Risk, which were really engaging for students who knew those games, and a drudgery for those who didn’t know the games. (Implementing pages of rules for a game you’ve never played is dull.) There were cultural biases in the choices of games, e.g., favoring the kinds of games that, in the US, middle class kids in Suburbia played.
The question to which I don’t know the answer is whether it’s possible to build “neutral” curriculum. The academic answer seems to be “no,” but it’s still an issue being explored. Some of what I’ve found from some digging:
- The prevailing academic answer says curricula are not neutral. A.V. Kelly’s 2009 book (5th ed) The Curriculum: Theory and Practice says that all approaches to curricular planning have a variety of biases in them. I found an interesting 2003 journal article that says that that’s not a bad thing (as well as other articles making a similar argument). Curricular change occurs because of particular strengths/weaknesses of a curriculum and are implemented through leveraging power relations. The challenge is being aware of the biases.
- That doesn’t mean that there aren’t efforts to create neutral curricula. In 2004, the UN announced an effort to create a culture-neutral school curriculum. I found an announcement for an on-going research project that is attempting to build gender neutral curricula. I found no results on any of these or similar attempts.
- I suspect that some computer scientists would say, “Use Mathematics. Math is neutral.” I found that the mathematics education community (at least in the articles I found that describing efforts to create neutral curriculum) believes that mathematics is neutral, but mathematics teaching is necessarily value-laden. I found a nice summary of the claim in Rethinking Mathematics piece at the Rethinking Schools site.
Simply put, teaching math in a neutral manner is not possible. No math teaching — no teaching of any kind, for that matter — is actually “neutral,” although some teachers may be unaware of this. As historian Howard Zinn once wrote: “In a world where justice is maldistributed, there is no such thing as a neutral or representative recapitulation of the facts.”
Bottom line is that I don’t think that anyone can answer Erik’s question. Maybe the academics are wrong and it’s possible to build neutral curricula — there certainly. are attempts today. However, if we don’t know if we can build it, then we definitely don’t have any to compare.
US Dept of Ed says CS is part of STEM
A note follows from Susan Rodger to ACM SIGCSE members, from her position on the ACM Education Policy Committee. This is great news! Cameron Wilson showed us this at the ACM Education Council meeting last weekend — the quoted statement showed up in the Federal Register, so it’s citable:
As a member of the ACM Education Policy Committee I wanted to make SIGCSE members aware of two important items.
1) First, the Department of Education has recognized computer science as a
science part of STEM. This is important for applying for funds related to
STEM.“Consistent with the Race to the Top Fund program, the Department interprets
the core academic subject of science under section 9101(11) to include
STEM education (science, technology, engineering and mathematics) which
encompasses a wide-range of disciplines, including computer science.”2) The Department of Education has two funds to apply for:
a) Invest in Innovation Fund (I3)
You can apply for these funds. A letter of intent is due April 1.b) Race to the Top
Only states can apply for these funds, but you can contact your
state department of education and point out to them that computer
science is an eligible discipline and ask how computer science
education fits into your state’s plan.For more details, please see this memorandum from ACM:
http://www.cs.duke.edu/csed/acmpolicy/RTTT_i3_Funding_Memo_v2.pdf
Susan===========================================================================
Susan Rodger, Professor of the Practice
Dept. of Computer Science, Box 90129
LSRC Room D237
Duke University, Durham, NC 27708-0129
Computing Ed Research - Guzdial's Take 
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