Posts tagged ‘broadening participation in computing’
Last year, Peter Denning approached me about contributing a post to an on-line Symposium that he was going to hold in the ACM Ubiquity magazine. The opening statement was written by Candace Thille — I am a big fan of Candace’s work, and I really liked her statement. I agreed to provide a response for the symposium.
Back in May, when I originally wrote the ending, I was concerned that so many Computer Scientists were working in MOOCs. MOOCs don’t address the critical needs of CS education, which are broadening participation and preparing more teachers. The real worry I had was that MOOCs would suck all the air out of the room. When all the attention is going to MOOCs, not enough attention is going to meeting our real needs. MOOCs are a solution in search of a problem, when we already have big problems with too few solutions.
My original ending took off from Cameron Wilson’s (then director of public policy for ACM, now COO of Code.org) call for “All Hands on Deck” to address issues of broadening participation and teacher professional development. Extending the metaphor, I suggested that the computer scientists working on MOOCs had gone “AWOL.” They were deserters from the main front for CS education.
This was the first article that I’ve ever written where the editor sent it back saying (paraphrased), “Lighten up, man.” I agreed. I wrote the new conclusion (below). MOOCs are worth exploring, and are clearly attractive for computer scientists to work on. Researchers should explore the avenues that they think are most interesting and most promising.
I’m still worried that we need more attention on challenges in computing education, and I still think that MOOCs won’t get us there. Critiquing MOOC proponents for not working on CS ed issues will not get us to solutions any faster. But I do plan to keep prodding and cajoling folks to turn attention to computing education.
Here’s the new ending to the paper:
MOOCs may be bringing the American university to an end—a tsunami wiping out higher education. Given that MOOCs are least effective for our most at-risk students, replacing existing courses and degrees with MOOCs is the wrong direction. We would be tailoring higher education only to those who already succeed well at the current models, where we ought to be broadening our offerings to support more students.
Computer science owns the MOOC movement. MOOC companies were started by faculty from computing, and the first MOOC courses were in computer science. One might expect that our educational advances should address our educational problems. In computing education, our most significant educational challenges are to educate a diverse audience, and to educate non-IT professionals, such as teachers. MOOCs are unlikely to help with either of these right now—and that’s surprising.
The allure of MOOCs for computer scientists is obvious. It’s a bright, shiny new technology. Computer scientists are expert at exploring the potential of new computing technology. However, we should be careful not to let “the shoemaker’s children go barefoot.” As we develop MOOC technology, let’s aim to address our educational problems. And if we can’t address the problems with MOOC technology, let’s look for other answers. Computing education is too important for our community and for our society.
Salon.com wrote about the boycott that’s emerging because a major chemistry conference is all male. The linked article, from the President of the University of Cincinnati, talks about what’s needed to retain and grow women in STEM. I wouldn’t have guessed that we’d have this problem in Chemistry before Computer Science.
The recent threat to boycott an upcoming international chemistry conference because of its all-male speaking program reminds us how far we still have to go when it comes to women in the science, technology, engineering and math (STEM) fields. The challenge remains that many STEM professions remain male-dominated, especially in academia.
Yup, Herminia has the problem right — if CS MOOCs are even more white and male than our face-to-face CS classes, and if hiring starts to rely on big data from MOOCs, we become even less diverse.
But that’s just the tip of the iceberg. One of the developments that will undoubtedly cement the relationship between big data and talent processes is the rise of massive open online courses, or MOOCs. Business schools are jumping into them whole hog. Soon, your MOOC performance will be sold to online recruiters taking advantage of the kinds of information that big data allows—fine distinctions not only on content assimilation but also participation, contribution to, and status within associated online communities. But what if these new possibilities—used by recruiters and managers to efficiently and objectively get the best talent—only bake in current inequities? Or create new ones?
I’ve been excited to see this paper get published since Betsy first told me about the work. The paper described below (by Betsy DiSalvo, Cecili Reid, and Parisa Khanipour Roshan) looks at the terms that families commonly use to find on-line resources to help their children learn about computer science. They didn’t find Alice or Scratch or Blockly — none of the things that would be our first choices for CS education opportunities on-line. Betsy and her students show how we accidentally hide our resources from the uneducated and under-privileged, by presuming that the searchers are well-educated and privileged. They point out that this is one way that open education resources actually actually increase the socioeconomic gap, by not being easily discoverable by those without privilege. I got to see a preview of this talk, and the results are surprising — a video of the preview talk will be available here. Friday March 7, 3:45-5, in Room Hanover DE.
They Can’t Find Us: The Search for Informal CS Education
In this study we found that search terms that would likely be used by parents to find out-of-school computer science (CS) learning opportunities for their children yielded remarkably unproductive results. This is important to the field of CS education because, to date, there is no empirical evidence that demonstrates how a lack of CS vocabulary is a barrier to accessing informal CS learning opportunities. This study focuses on the experience of parents who do not have the privilege of education and technical experience when searching for learning opportunities for their children. The findings presented will demonstrate that issues of access to CS education go beyond technical means, and include ability to conduct suitable searches and identify appropriate computational learning tools. Out-of-school learning is an important factor in who is motivated and prepared to study computer science in college. It is likely that without early access to informal CS learning, fewer students are motivated to explore CS in formal classrooms.
Here’s a great answer to the under-representation on the AP CS — the College Board (with funding from Google) will offer grants to help start AP programs, including AP CS (see details for AP CS for STEM Access).
AP STEM Access Program: In fall 2013, the College Board implemented the AP STEM Access program in 335 public high schools across the country. With the support of a $5 million Google Global Impact Award to DonorsChoose.org, these schools started offering new AP math and science courses with the goal of enabling underrepresented minority and female students who have demonstrated strong academic potential to enroll in and explore these areas of study and related careers. Over the next three years, the AP STEM Access program will give an estimated 36,000 students the opportunity to study college-level STEM course work in these newly offered AP classes.
Farnam Jahanian visited Georgia Tech last month. Farnam is the Assistant Director at the US National Science Foundation, in charge of all computing related funding (CISE Division). He spoke to issues about computing education funding, and I got to ask some of my questions, too.
He said that the Office of Management and Budget has really been driving the effort to consolidate STEM education funding programs. OMB was unhappy that Biology, Engineering, and CISE all had their own STEM education programs. However, CISE got to keep their education research program (as the new STEM-C program) because it was already a collaboration with the education division in NSF (EHR). All the rest (including TUES) is being collapsed into the new EHR programs.
In his talk, he made an explicit argument which I’ve heard Jan Cuny make, but hadn’t heard an NSF AD make previously:
- We have a dramatic underproduction of computing degrees, around 40K per year.
- We have a dramatic under-representation of certain demographic groups (e.g., women, African-Americans, Hispanics), and we can’t solve #1 without solving that under-representation. He says that the basic arithmetic won’t work. We can’t get enough graduates unless we broaden participation in computing.
- We have a lack of presence in primary and secondary school in the United States (K-12). He claims that we can’t solve #2 without fixing #3. We have to have a presence so that women and under-represented minority groups will discover computing and pursue degrees (and careers) in it.
We’ve heard stories like this before, about the implicit bias in how STEM professionals are judged. This one is striking because the participants are graduate students, not established researchers who reflect years of experience in the community. These are the new researchers, and they’re already biased.
The research found that graduate students in communication — both men and women — showed significant bias against study abstracts they read whose authors had female names like “Brenda Collins” or “Melissa Jordan.”
These students gave higher ratings to the exact same abstracts when the authors were identified with male names like “Andrew Stone” or “Matthew Webb.”
In addition, the results suggested that some research topics were seen as more appropriate for women scholars — such as parenting and body image — while others, like politics, were viewed as more appropriate for men.
These findings suggest that women may still have a more difficult time than men succeeding in academic science, said Silvia Knobloch-Westerwick, lead author of the study and associate professor of communication at The Ohio State University.
“There’s still a stereotype in our society that science is a more appropriate career for men than it is for women,” Knobloch-Westerwick said.
Reminds me of the Jump$start program that made students over-confident and worse at making financial decisions. Teach people a little about diversity, and they think it doesn’t exist anymore.
Diversity training programs lead people to believe that work environments are fair even when given evidence of hiring, promotion or salary inequities, according to new findings by psychologists at the University of Washington and other universities.
The study also revealed that participants, all of whom were white, were less likely to take discrimination complaints seriously against companies who had diversity programs.
My thinking on computing education has been significantly influenced by a podcast about hand-washing and financial illiteracy. I suspect that education is an ineffective strategy for achieving the goal of Computing Literacy for Everyone. I have a greater appreciation for work like Alan Kay’s on STEPS, Andy Ko’s work on tools for end-user programming, and the work on Racket.
On Hand-Washing and Financial Illiteracy
I have been listening to Freakonomics podcasts on long drives. Last month, I listened to “What do hand-washing and financial illiteracy have in common?” I listened to it again over the next few days, and started digging into the literature they cited.
At hospitals, hand-washing is far less common than our knowledge of germ theory says it ought to be. What’s most surprising is that doctors, the ones with the most education in the hospital, are the least likely to wash their hands often enough. The podcast describes how one hospital was able to improve their hand-washing rates through other behavioral methods, like shaming those who didn’t wash their hands and providing evidence that their hands were likely to be filled with bacteria. More education doesn’t necessarily lead to behavioral change.
Much more important was the segment on financial illiteracy. First, they present the work of Annamuria Lusardia who has directly measured the amazing financial illiteracy in our country. There is evidence that much of the Great Recession was caused by poor financial decisions by individuals. Less than a third of the over-50-year-old Americans that Lusardia studied could correctly answer the question, “If you put $100 in a savings account with 2% annual interest, at the end of five years you will have (a) less than $102, (b) exactly $102, or (c) more than $102?” More mathematics background did lead to more success on her questions, but she calls for a much more concerted effort in financial education. Her arguments are supported by some pretty influential officials, like Fed Reserve Chair Ben Bernanke and former Secretary of the Treasury Paul O’Neill. It makes sense: If people lack knowledge, we should teach them.
Lauren Willis strongly disagrees, and she’s got the data to back up her argument. She has a 2008 paper with the shocking title, Against Financial Literacy Education that I highly recommend. She presents evidence that financial literacy education has not worked — not that it couldn’t work, but it isn’t working. She cited several studies that showed negative effects of financial education. For example, high school students who participated in the Jump$start program become much more confident about their ability to make financial decisions, and yet made worse decisions than those students who did not participate in the program.
The problem is that financial decisions are just too complicated, and education (especially universal education) is expensive to do well (though Willis doesn’t offer an estimated cost). Educational curricula (even if tested successful) is not always implemented well. The gap between education in teen years and making decisions in your 40’s and 50’s is huge. Instead of education, we should try to prevent damage from ignorance. Willis suggests that we should create a cadre professional of financial advisors and make them available to everyone (for some “pro bono”), and that we should increase regulation of financial markets so that there are fewer riskier investments for the general public. It costs the entire society enormously when large numbers of people make poor financial decisions, and it’s even more expensive to provide enough education to prevent the cost of all that ignorance.
This was a radical idea for me. Education is not free, and sometimes it’s cheaper to prevent the damage of ignorance than correcting the ignorance.
Implications for Computing Literacy Education
I share the vision of Andy DiSessa and others of computing as a kind of literacy, and a goal of “Computing for All” where everyone has the knowledge and facility to build programs (for modeling, simulations, data analyses, etc.) for their needs. Let’s call that a goal of “Universal Computing Literacy,” and we can consider the costs of using education to reach that goal, e.g., “Universal Computing Education to achieve Universal Computing Literacy.”
The challenge of computing literacy may be even greater than the challenge of financial literacy. People know even less about computing than they do about finance. We don’t know the costs of that ignorance, but we do know that it has been difficult and expensive to provide enough education to correct that ignorance.
Computing may be even more complicated than finance. Willis talks about the myriad terms that people need to know to make good financial decisions (like “adjustable rate mortgages”), but they are at least compounds of English words! I attended a student talk this week, where terms like “D3” and “GreaseMonkey” were bandied about like they were common knowledge. We invent so much language all the time.
The problem is that education is often inefficient and ineffective. Jeremy Roschelle pointed out that education improvements rarely impact economic outputs. Greg Wilson shared a great paper with me in response to some tweets I sent about these ideas. Americans have always turned to education to solve a wide variety of ills, but surprisingly, without much evidence of efficacy. We can teach kids all about healthy eating, but we still have a lot of obesity. Smokers often know lots of details about how bad smoking is for them. Education does not guarantee a change of behavior. This doesn’t mean that education could not be made more effective and more efficient. But it might be even more expensive to fix education than to deal with ignorance.
Universal education is always going to be expensive, and some things are worth it. Text illiteracy and innumeracy are very expensive for our society. We need to address those, and we’re not doing a great job at that yet. Computing education to achieve real literacy is just not as important.
I am no longer convinced that providing computing education to everyone is going to be effective to reach the goal of making everyone computing literate, and I am quite convinced that it will be very expensive. Requiring computing education for STEM professionals at undergraduate level may still be cost-effective, because those are the professionals most likely to see the value of computing in their careers, which reduces the costs and makes the education more likely to be effective.
Barb sees a benefit in Universal Computing Education, but not to achieve Universal Computing Literacy. We need to make computing education available everywhere for broadening participation in computing. To get computing into every school, Barb argues that we have to make it required for everyone. Without the requirement, schools won’t go to the effort of including it. Without a requirement, female and URM students who might not see themselves in computing, would never even give it a chance. In response to my argument about cost, she argues that the computing education for everyone doesn’t have to be effective. We don’t have to achieve lifelong literacy for everyone. Merely, it has to give everyone exposure, to give everyone the opportunity to discover a love for computing. Those that find that love will educate themselves and/or will pursue more educational opportunities later. I heard Mike Eisenberg give a talk once many years ago, where he said something that still sticks with me: that the point of school is to give everyone the opportunity to find out what they’re passionate about. For that reason, we have to give everyone the chance to discover computing, and requiring it may be the only way to reach that goal.
It’s always possible that we’ll figure out to educate more effectively at lower cost. For example, integrating computing literacy education into mathematics and science classes may be cheaper — students will be using it in context, teachers in STEM are better prepared to learn and teach computing, and we may improve mathematics and science teaching along the way. My argument about being too expensive is based on what we know now how to do. Economic arguments are often changed by improved science (see Malthus).
As Willis suggests for financial literacy, we in computing literacy are probably going to be more successful for less cost by focusing on the demand side of the equation. We need to make computing easier, and make tools and languages that are more accessible, as Alan Kay, Andy Ko, and the Racket folks are doing. We have to figure out how to change computing so that it’s possible to learn and use it over an entire career, without a PhD in Computer Science. We have to figure out how to get these tools into use so that students see use of such tools as authentic and not a “toy.”
“Computing for All” is an important goal. “Access to Computing Education for All” is critical. “Universal Computing Education to achieve Universal Computing Literacy” is likely to be ineffective and will be very expensive. On the other hand, requiring computing education may be the only way to broaden participation in computing.
I’ve already written a couple of SIGCSE Symposium 2013 preview posts (on the Dorn and Elliott Tew paper, and on the UCSD set of papers on Peer Instruction). Here in my last preview post, I’ll give you a sense for what I’ll be up to. I fly out to Denver Tuesday 5 March in the evening.
- Wednesday (6 March 2013), I’ll be at the SIGCSE Board Meeting all day. If I figured it right, this is my last face-to-face Board meeting — I’ve decided not to run again and I think that the new Board starts this Fall.
- Thursday, I have no presentations, but I have the day pretty much booked meeting with people who are also going to be at SIGCSE. Should be fun!
- Friday is over-booked.
- At 10:45 in Governors 12, Betsy DiSalvo is presenting her paper on Glitch (that I’m a co-author on), “Workifying Games.”
- We’re having an ECEP lunch for advisors and Experts Bureau members at noon. (I didn’t realize until this weekend that there’s a plenary on Friday at lunchtime — that’s never happened before that I can recall at the SIGCSE Symposium.)
- At 1:45 in Ballroom E, I’m on the “Passion, Beauty, Joy, and Awe” panel — I’ve decided to try to do a live coding with sound demo, which should be exciting and (maybe) fun and (maybe) disastrous.
- At 3:45 in Ballroom E, I’m on the Panel on MOOCs, “The Revolution will Be Televised: Perspectives on Massive Open Online Education,” with both proponents and critics. (Guess which role I’ll be playing.)
- I’m having a dinner with student volunteers at 5 pm, then hoping to find Michael Köllig to congratulate him on his Outstanding Contribution to CS Education award.
- Saturday is literally double-booked.
- At 9 am, I’m presenting in the “Nifty Assignments” special session in Ballroom E, AND I’m a discussant in Ballroom F on the “Expanding access to K-12 Computer Science Education: Research on the Landscape of CS Professional Development.” I’m thrilled to be on “Nifty” for the first time, but which I could stay for both. I think I’m going to speak first at “Nifty,” then run next door to be on the “Landscape” panel.
- At 3 pm, we start the ACM Education Council meeting (after Jane Margolis’s lunchtime plenary talk).
- Sunday, I’ll be at the ACM Education Council meeting all day, then fly home at 5, getting home at 10 pm. Monday is our PhD recruiting day and teaching, so not much recovery and decompression time.
(If I miss some days of the blog in here, I hope you’ll understand.)
In the last three weeks, I was asked several times at MIT and Stanford about what questions I would like answered about MOOCs. I didn’t get any answers, but folks at Georgia Tech were asking me about the questions, so I thought I’d share some of them here. This is the evidence I’m looking for.
“Florida is killing Computer Science,” was the first thing that Joanne Barrett told us when we asked her how things were going in Florida. Barbara and I went to Orlando to give the Technology track keynote (joint! It was fun!) and two breakouts at the FCIS Conference on Thursday. Joanne ran the Technology track at FCIS. (Our travel was sponsored by CSTA and Google – thanks!) The mood of the CS teachers we met was dismal.
Currently, computer science is part of the academic high school degree in Florida — the classes that one would take as College preparation. It’s mostly taught by mathematics teachers. This year is the end of that. This is the last year that the current CS classes will be offered.
As of next year, all the computer science classes in Florida will be moved into business, as part of career preparation. As we understand it from Joanne, they literally won’t count for credit towards an academic high school degree. The AP CS will stay in the academic track, but all the other computer science courses will move to business.
Why? Exactly the same issue as in Georgia: Perkins funding will pay for hardware, so career prep has the computers, and it gets computer science. We spoke to one business teacher who is desperately seeking professional development to prepare herself for teaching all these new computing courses. We met one of the teachers at the Florida Virtual High School (which has a really cool CS sequence, and an astounding success rate for their students on AP CS), and she said that they may not even be able to offer any CS next year. FVHS is about academic subjects, and CS is being re-classified. Florida is also looking for industry certification for the end of the Perkins-funded pathway, and the teachers we talked to said that they’re currently considering an IEEE Certification — which is explicitly for graduates of four year degree programs, not high school students.
What will this do to CS education in Florida? it won’t be “killed,” but it will be changed. I worry about the quality, when swapping out all the experienced math teachers for inexperienced business teachers. I can’t the impact on CS10K goals.
Can AP CS succeed (in particular, the new CS:Principles effort) as a standalone AP, with all the other CS courses in another track? Maybe. I wonder how much effort school districts will put into AP CS, if they have a different, funded CS pathway. I also wonder if CS:Principles can meet its goal of helping to broaden participation in this context — the career prep programs that I’ve seen are far more heavily under-represented minority than the college prep programs. What if the minority students you want to draw into computing via AP CS are off taking the career prep classes?
Bravo to Dr. Chuck Severance for sharing the maps he created of where his MOOC students are coming from! These are fascinating data, and the result is particularly useful since there’s so little data to be had on MOOCs. It’s hard to be sure from just eye-balling the data, but my sense is:
- This is mostly a developed-world phenomenon.
- A small percentage of attendees come from Africa.
I would really love to know gender and ethnicity/race demographics on who is taking and who is completing MOOCs. Here’s a prediction for the technology-heavy Udacity courses: 80% White or Asian, 90% male. Anybody know where we can get the data to test this hypothesis?
As part of my Internet History, Technology, and Security course on Coursera I did a demographics survey and received 4701 responses from my students.
I will publish all the data in a recorded lecture summarizing the class, but I wanted to give a sneak preview of some of the geographic data results because the Python code to retrieve the data was fun to build. Click on each image to play with a zoomable map of the visualized data in a new window. At the end of the post, I describe how the data was gathered, processed and visualized.
Nice piece in Time on the lack of computing education in the United States. In particular, I like that Jane Margolis takes on the myth that students will just learn it on their own without support. That’s thinking that prevents broadening participation in computing
Not every kid has those advantages.
“There is this assumption that if you have this innate talent and you’re drawn to it, you’ll learn it on your own and you don’t really need it at school,” says Jane Margolis, senior researcher at UCLA’s Graduate School of Education and Information Studies and author of Stuck in the Shallow End: Education, Race, and Computing. “Kids that have a lot of resources at home, often with parents with a lot of technical know-how and access to software, people look at them and say ‘Oh, they just take to it.’”
In 2010, the San Jose Mercury News reported that the percentage of computer workers in Silicon Valley that were black or Latino stood at 1.5% and 4.7%, respectively. Girls Who Code, an organization that encourages teen girls to pursue opportunities in technology, points out that only 14% of undergraduate computer science degrees are earned by women.
I love the reasoning behind this study. We know that gender gaps in achievement (and enrollment?) are entirely cultural. So the solution is cultural, and has nothing to do with the topic. Here, an essay in Physics class narrowed the gender gap. Could something like this work in CS, too? (Thanks to Fred Martin for sending this to me!)
A study in last week’s Science describes a program at the University of Colorado, focused on helping to narrow the achievement gap in an introduction to physics class targeted to science majors. In past years, research had found that a strong background and preparation could account for over half the gender difference in test scores, but that still leaves other, substantial factors to explain the discrepancies.
The authors suspected that stereotypes might account for the remaining differences. “The fear of being devalued based on a group identity, such as becoming aware that one could be seen in light of a negative stereotype about one’s group, has been shown to undermine performance on difficult tests,” they explain. “For example, women’s performance on difficult math and science tests can suffer insofar as they worry that their poor performance could be seen to confirm a negative gender stereotype.”
Since the problem wasn’t physics-based, the solution wasn’t either.