Archive for December, 2011
The WordPress.com stats helper monkeys prepared a 2011 annual report for this blog.
Here’s an excerpt:
The Louvre Museum has 8.5 million visitors per year. This blog was viewed about 170,000 times in 2011. If it were an exhibit at the Louvre Museum, it would take about 7 days for that many people to see it.
Here’s a meta-post as the last post of ComputingEd 2011. Below is 3 of the top 10 CSTA Blog posts of 2011 with a link to the rest of the list, and Alfred Thompson has also posted his most-read blog posts of 2011. Alfred has also created a nice roll of CS Ed blogs. Here’s to a great 2012!
10. January 12, 2011- A Joint Call for Research Why Computer Science Education is Important for K-12. This post is well worth a second look to provide a good reminder that we make many statements about the necessity of CS for all, but we need to get more research behind that!
9. February 21, 2011- Election Data And Socially Relevant Computing. Something to think about with our upcoming National election in 2012.
8. March 9, 2011- Top 10 Reasons Why You Should Attend SIGCSE. Yes, a gratuitous self-re-posting of another Top 10 List. But let it serve as a reminder to think about attending SIGCSE in North Carolina this year!
NBC News’ Education Week asked me to write a guest blog post two weeks ago on my argument for why we should teach more CS classes. It got posted yesterday. I am grateful to Amy Bruckman, Christine Alvarado, Barbara Ericson, and Brendan Streich for their great comments and even text that they provided for this. We worked hard on those few hundred words!
Most people who write computer programs aren’t professional programmers. Scientists and engineers write programs on a daily basis. But even non-technical professionals rely on deep knowledge of computing. Graphic designers work with many images with multiple layers, and they write programs to automate operations. An estimate out of Carnegie Mellon University says that for every professional software developer in 2012, there will be four people who write programs but aren’t professional software developers.
Here’s the problem with this picture: Few of those non-professional programmers had any computer science (CS) classes. Either the CS classes weren’t there, or they avoided them. Research at Georgia Tech has found that pre-teen Girl Scouts already think computer science is “geeky.” Brian Dorn, an assistant professor at University of Hartford, found that even adult graphic designers think computer science is “boring,” and they avoid classes in computer science.
I’m not sure that I follow the whole argument (e.g., can we extrapolate from offensive behavior at conferences to the macro-level movement of women away from IT?), but I find the detail and analysis here quite interesting. The problem of mid-career drop-out is real, and we need to figure out ways of coming back into the workforce.
Though not the only reason, the numbers show that in software development specifically, sexist and overtly offensive behavior (both online and off) is one of two key factors as to why women are leaving technical fields as reported by ABC News. This article quotes Laura Sherbin, director at the Center for Work-Life Policy, who published a study in the Harvard Business Review titled “Reversing the Brain Drain in Science, Engineering, and Technology”. Laura goes on to explain the primary reasons women leave STEM fields…
“The top two reasons why women leave are the hostile macho cultures — the hard hat culture of engineering, the geek culture of technology or the lab culture of science … and extreme work pressures”
Sherbin also highlights just how critical and surprising the numbers are, even to researchers.
“The dropping out was a surprise to us. We knew anecdotally that women were leaving these careers. We didn’t expect to see the number 52 percent.”
Interesting response to President Obama’s call for creating many more engineers, which has started from the claim that we’re not being competitive with China’s production of engineers. This article from the Washington Post suggests that there isn’t a shortage of engineers at all in the US. It feels like the problem of determining whether or not we have enough CS enrollment – what’s “enough”?
What’s more, China’s tally of 350,000 was suspect because China’s definition of “engineering” was not consistent with that of U.S. educators. Some “engineers” were auto mechanics or technicians, for example. We didn’t dispute that China was and is dramatically increasing its output of what it calls engineers. This year, China will graduate more than 1 million (and India, close to 500,000). But the skills of these engineers are so poor that comparisons don’t make sense. We predicted that Chinese engineers would face unemployment. Indeed, media reports have confirmed that the majority of Chinese engineers don’t take engineering jobs but become bureaucrats or factory workers.
Then there is the question of whether there is a shortage of engineers in the United States. Salaries are the best indicator of shortages. In most engineering professions, salaries have not increased more than inflation over the past two decades. But in some specialized fields of software engineering in Silicon Valley and in professions such as petroleum engineering, there have been huge spikes. The short answer is that there are shortages in specific fields and in specific regions, but not overall. Graduating more of the wrong types of engineers is likely to increase unemployment rather than create jobs.
Here’s an interesting view to contrast with the student-as-consumer view that sometimes drives master teachers out of their jobs (since master teachers make students work). Nobody will ever say that Steve Jobs ignored the consumer — Apple didn’t get to be the huge consumer electronics company today without trying to please the consumer. But he never asked them what they wanted. They did get a vote: To buy, or not buy. Are there lessons for us in education? Are course evaluations the education equivalent of market research?
In the Preface to Inventing the Medium, I write about the limitations of asking users what they want since people “often cannot think past the familiar conventions of existing devices and applications.” A similar theme emerged from the many admiring reminiscences that followed Steve Jobs’ death this month. From the New York Times obituary : When asked what market research went into the iPad, Mr. Jobs replied: “None. It’s not the consumers’ job to know what they want.”
Interesting piece that takes a regional perspective on undergraduate CS enrollment. The suggestion is that New York is seeing a big growth in computer science because the focus there is across disciplines, not just technology for technology sake (as in Silicon Valley). It’s based on a notion that computing is “a basic skill in the 21st century.”
The number of declared undergraduate computer science majors at the Columbia University School of Engineering and Applied Science jumped 12% this year over last year; at New York University, the number rose 10%. Queens College and Stevens Institute of Technology in Hoboken, N.J., also reported jumps in the number of computer science majors. At the same time, the number of students enrolled in computer science classes has surged between 30% and 50%, professors said.
Remember the old Pogo comic strip, “We have met the enemy and he is us.”? This study suggests that we have found a similar problem in promoting more women and minorities in STEM.
The survey showed that 40 percent of minority and female chemists and engineers polled said they were discouraged from studying STEM subjects. Forty-four percent said their college professors were the sources of the discouragement.
“We wanted to find out if this discouragement is still occurring,” said Mae Jemison, a physician, astronaut, college professor and Bayer spokesperson for the company’s Making Science Make Sense Initiative. “We found out it is.”
Earlier this year, I talked about Seymour Papert’s encouragement to challenge yourself as a learner, in order to gain insight into learning and teaching. I used my first-time experiences working on a play as an example.
I was in my first choir for a only year when our first child was born. I was 28 when I first started trying to figure out if I was a bass or tenor (and even learn what those terms meant). Three children and 20 years later, our children can get themselves to and from church on their own. In September, I again joined our church choir. I am pretty close to a complete novice–I have hardly even had to read a bass clef in the last two decades.
Singing in the choir has the most unwritten, folklore knowledge of any activity I’ve ever been involved with. We will be singing something, and I can tell that what we sang was not what was in the music. ”Oh, yeah. We do it differently,” someone will explain. Everyone just remembers so many pieces and how this choir sings them. Sometimes we are given pieces like the one pictured above. It’s just words with chords and some hand-written notes on the photocopy. We sing in harmony for this (I sing bass). As the choir director says when he hands out pieces like this, “You all know this one.” And on average, he’s right. My wife has been singing in the choir for 13 years now, and that’s about average. People measure their time in this choir in decades. The harmony for songs like this were worked out years and years ago, and just about everyone does know it. There are few new people each year — “new” includes even those 3 years in. (Puts the “long” four years of undergraduate in new perspective for me.) The choir does help the newcomers. One of the most senior bass singers gives me hand gestures to help me figure out when next phrase is going up or down in pitch. But the gap between “novice+help” and “average” is still enormous.
Lave and Wenger in their book “Situated Learning” talk about learning situations like these. The choir is a community of practice. There are people who are central to the practice, and there are novices like me. There is a learning path that leads novices into the center.
The choir is an unusual community of practice in that physical positioning in the choir is the opposite of position with respect to the community. The newbies (like me) are put in the center of our section. That helps us to hear where we need to be when singing. The more experienced people are on the outside. The most experienced person in the choir, who may also be the eldest, tends to sit on the sidelines, rather than stand with the rest of the choir. He nails every note, with perfect pitch and timing.
Being a novice in the choir is enormous cognitive overload. As we sing each piece, I am reading the music (which I’m not too good at) to figure out what I’m singing and where we’re going. I am watching the conductor to make sure that my timing is right and matches everyone else. I am listening intently to the others in my section to check my pitch (especially important for when there is no music!). Most choir members have sung these pieces for ages and have memorized their phrasing, so they really just watch the director to get synchronized.
When the director introduces a new piece of music with, “Now this one has some tricky parts,” I groan to myself. It’s “tricky” for the average choir members — those who read the music and who have lots of experience. It’s “tricky” for those with literacy and fluency. For me, still struggling with the notation, it takes me awhile to get each piece, to understand how our harmony will blend with the other parts.
I think often about my students learning Java while I am in choir. In my class, I introduce “tricky” ideas like walking a tree or network, both iteratively and recursively, and they are still struggling with type declarations and public static void main. I noticed last year that many of my students’ questions were answered by me just helping them use the right language to ask their question correctly. How hard it must be for them to listen to me in lecture, read the programs we’re studying, and still try to get the “tricky” big picture of operations over dynamic data structures–when they still struggle with what the words mean in the programs.
Unlike working on the play, singing in the choir doesn’t take an enormous time investment — we rehearse for two hours one night, and an hour before mass. I’m having a lot of fun, and hope to stick with it long enough to move out of the newbie class. What’s motivating me to stick with it is enjoyment of the music and of becoming part of the community. There’s another good lesson for computer science classes looking to improve retention. Retention is about enjoying the content and enjoying the community you’re joining.
I’m intrigued with why the Ladies Learning Code workshops are successful (in terms of number and diversity of attendees). There are other sources for the same information, and there are even better educational opportunities. Is it the fact that these are successful women teaching other women about technology? Who the teacher is matters.
We find similar outcomes from our GaComputes weekend workshops led by Georgia Tech undergraduates. If a female leads the workshop, the boys in the workshop (more than the girls) tend to improve their attitudes about the statement “Girls can do computing,” likely because they have evidence that there are females who rock at programming. How the leaders teach is important apart from gender of the teacher. We found that the female leaders sometimes had girls in a workshop exhibiting worse attitudes about computing, e.g., the girls afterward felt less confident about using computing. From our external evaluator’s observations, we found the female leaders were more likely to make hesitant statements about programming like, “I know this is really hard, but it will be worth it.” The male leaders tended to be more gung-ho, “This is gonna be great!” Attitude also matters.
I found that TheStar.com link below doesn’t always work — here’s another good article on Ladies Learning Code, if the link below fails.
These women are working to close the technology gender gap. Studies have documented the pervasive divide in high-tech, science, engineering and math sectors. Statistics Canada says women account for just over 20 per cent of the workforce, with little change over two decades.
“They’re interested in honing their skill set, learning some technical skills, learning how to communicate with developers and broaden their horizons,” says Heather Payne, 24, founder of Ladies Learning Code, which provides computer programming for beginners.
Four months after Payne founded the group — with a tweet — these women (and a few men) are learning Ruby, the programming language on which Twitter is built.
160,000 Enroll Stanford’s Online AI Course—Is the University Obsolete?
@aiclass: “Amazing we can probably offer a Master’s degree of Stanford quality for FREE. HOW COOL IS THAT?”—September 23, 2011
Mark blogged about Stanford’s online Artificial Intelligence course in August. I’ve been leading a group of UMass Lowell students, who are following the course and will receive regular course credit under my supervision.
Sebastian Thrun and Peter Norvig’s online course, Introduction to Artificial Intelligence, was announced via email to a AAAI list early last summer. The idea went viral. Articles were published in the New York Times, the Chronicle of Higher Education, and many media outlets.
The course was advertised as equivalent to the Stanford University undergraduate AI course. As Thrun posted on Twitter:
@aiclass: “Advanced students will complete the same homeworks and exams as Stanford students. So the courses will be equal in rigor.”—September 28, 2011
The course launched the first week of October. 160,000 students had signed up.
At UMass Lowell
I had taught my department’s AI course in Fall 2010. Students started asking me if they could take the Stanford course as a directed study. In August, I decided to open two full course sections (grad and undergrad, meeting jointly). I wanted to experience this first-hand.
I told students they would be responsible for a final project on top of the Stanford requirements.
I ended up with 16 students—12 grad and 4 undergrad. We meet once weekly for 75-minutes, seminar-style, and talk about the Stanford material after each week’s assignment is already due.
How It Works
The Stanford course consists of weekly lectures—two or three 45-minute topics that are broken up into 15 or 20 short videos. Many of the individual videos have embedded questions (multiple-choice or fill-in-the-value). Thrun’s colleague at the Stanford AI Lab, Prof. Daphne Koller, is a pioneer of this approach, and discussed it in a recent NYT essay.
At the end of these mini-lessons, the video image transforms web form where you fill in the answers. You’ve already logged into the class server, and it grades you immediately. After you submit, you are presented with a link for an explanation video.
The lectures themselves are inspired by Khan Academy’s approach. Occasionally, Thrun and Norvig will train the camera at themselves, but the core content is with the camera on a piece of paper, with Thrun or Norvig talking and writing in real time. Some people don’t like this format, but I find it relaxing and engaging. I like seeing equations written out with verbal narration in perfect sync.
There are weekly problem set “homeworks” with the same format. The homeworks track the lecture material very closely. If you paid attention and did the problems embedded in the lectures, the problem sets are usually easy.
There has been a midterm with the same format as the homeworks. It had 15 questions. There will be a final, and I expect the same format.
The homeworks, midterm, and final have hard deadlines and are only viewable to pre-registered, logged-in students who chose the “Advanced” track. The lectures are openly viewable by anyone. The server backend keeps track your scores on the homeworks, midterm, and final, which will count to your “grade”—a ranking within the active student cohort. There is a “Basic” track which consists only of the lectures.
Of the 160,000 people who initially registered, it was reported in early December that 34,000 students from the Advanced cohort are still actively participating.
On the Course
Thrun and Norvig are great teachers. Thrun is always visibly excited about teaching the material. Norvig is not as effusive, but you still know he really cares about the ideas. They’ve thought through excellent ways of explaining the ideas and quizzing the in-lecture comprehension checks. They often bring fun props or show research projects in the videos recordings.
Thrun and Norvig are only a week or so ahead of the course delivery, and they’re paying close attention their students’ progress. There is a lot of activity on the web forums. Students are completing assignments.
Thrun and Norvig have recorded several “office hours,” where students submit questions and vote on their favorite ones, and then they pick questions and answer them on camera.
In this way, the course is like a usual class—it’s not “canned.” Thrun’s and Norvig’s enthusiasm is infectious.
This is the old-fashioned emotional connection between teachers and their students.
These things make it seem more like a conventional course than you would expect.
My Role as a Teacher
I don’t have to lecture the material. When we meet, my students have (largely) worked through the lectures and homeworks.
So I don’t have to explain things to students for the first time. Instead, we use in-class time to have an interesting conversation about the parts of the material that people found confusing or disagreed upon. We’ve had some great arguments this semester.
This is a lot like the approach suggested in 2006 by Day and Foley in their HCI course at Georgia Tech. They recorded web lectures, and then used classroom time for hands-on learning activities.
Koller calls this “the flipped classroom.” She reports higher-than-usual attendance in her Stanford courses that are taught this way: “We can focus precious classroom time on more interactive problem-solving activities that achieve deeper understanding—and foster creativity.”
One fun thing for me is that Thrun and Norvig are really in charge, and I get to dig into a role as a learning coach. If something is confusing, I get to really be on the side of my students in helping figure it out. I personally really enjoy this. Others might not.
Overall—this is an exciting experiment. Thrun and Norvig have created a fantastic set of interactive lectures and some good quiz problems. They’ve put in a lot of work, and it shows.
What Does It Mean?
I don’t think this threatens the university in any profound way.
Thrun and Norvig are renowned scientists, and charismatic and thoughtful teachers. But if in some future semester, they, or others comparable, are not participating in real time, I wonder how many the 34,000 remaining students would be carrying forward with the determination that they now are.
Furthermore, in order to learn how to “do AI”—and not just learn “about AI”—students need to do significant implementation and research projects. They need individual, time-intensive guidance from a faculty member who can encourage them and help them sharpen their focus.
In Koller’s essay, she extols the new, deeper value of in-class time.
So, the flipped classroom doesn’t put us out of business. It makes us more valuable—but only if we take advantage of the opportunity.
This is cool on multiple levels. It’s cool that Dick Baldwin is working hard to make computer science (and other subjects) more accessible to blind students. The project is cool, for turning diagrams into an embossed representation that can be read by touch. It’s also cool that Dick sent us a note telling us that Barb’s Media Computation libraries were used in the student’s work. Dick is also making his MediaComp lectures available via video:
Baldwin, who came to ACC 18 years ago after a long career in digital computing, began creating an online tutorial that translates physics material into a format that can be read with an audio screen reader and an electronic Braille display tool. “Accessible Physics Concepts for Blind Students” is one of the only offerings of its kind.
“Blind students should not be excluded from physics courses because of inaccessible textbooks,” Baldwin says. While laws require that textbooks be accessible for students with disabilities through grade 12, there is no such mandate for college textbooks. “When a blind person goes to college and tries to take anything that involves anything other than words, they have a problem,” he says.
Greg Wilson is teaching an exciting new course for the Mozilla Foundation — he’s aiming to teach-the-teachers, helping groups that are teaching end-user programmers.
What do we know about how novices learn webcraft and programming, and how can we apply that knowledge to teaching free-range learners?
Right now, people all over the world are learning how to write programs and create web sites, but or every one who is doing it in a classroom there are a dozen free-range learners. This group will explore how we, as mentors, can best help them. Topics will include:
What does research tell us about how people learn? Why are the demographics of programming so unbalanced? What best practices in instructional design are relevant to free-range learners? What skills do people need in order to bake their own web? How are grassroots groups trying to teach these things now? What’s working and what isn’t?
We’ve had Jesse Heines of U. Massachusetts at Lowell visiting with us for the last couple weeks. He gave a GVU Brown Bag talk on Thursday about his Performamatics project — which has an article in this month’s IEEE Computer! Jesse has been teaching a cross-disciplinary course on computational thinking, where he team teaches with a music teacher. Students work in Scratch to explore real music and real computing. For example, they start out inventing musical notations for “found” instruments (like zipping and unzipping a coat), and talk about the kinds of notations we invent in computer science. I particularly enjoyed this video of the music teacher, Alex Ruthmann, performing an etude through live coding.
Jesse and I talked afterward: Where does this go from here? Where could Performamatics have its greatest impact? We talked about how these music examples could be used in introductory computing courses (CS1 and CS2), but that’s not what’s most exciting. Is the greatest potential impact of computing education creating more CS majors, creating more developers? Developers do have a lot of impact, because they build the software that fuels our world (or maybe, that eats our world). But developers don’t have a monopoly on impact.
I argued that the greatest impact for computing educators is on the non-majors and their attitudes about computing. I showed him some quotes that Brian Dorn collected in his ICER 2010 paper about adult graphics designers (who have similar educational backgrounds and interests to Jesse’s non-majors) on their attitudes about computer scientists:
P2: I went to a meeting for some kind of programmers, something or other. And they were OLD, and they were nerdy, and they were boring! And I’m like, this is not my personality. Like I can’t work with people like that. And they worked at like IBM, or places like that. They’ve been doing, they were working with Pascal. And I didn’t…I couldn’t see myself in that lifestyle for that long.
P5: I don’t know a whole ton of programmers, but the ones I know, they enjoy seeing them type up all these numbers and stuff and what it makes things do. Um, whereas I just do it, to get it done and to get paid. To be honest. The design aspect is what really interests me a lot more.
These are adults, perhaps not much different than your state or federal legislators, your school administrators, or even your CEO. Brian’s participants are adults who don’t think much of computer scientists and what they do. There are a lot of adults in the world who don’t think much of computer scientists, despite all evidence of the value of computing and computing professionals in our world.
Will Jesse’s students think the same things about computer scientists 5 years after his course? 10 years later? Or will they have new, better-informed views about computer science and computer scientists? The 2005 paper by Scaffidi, Shaw, and Myers predicted 3 million professional software developers in the US by 2012, and 13 million professionals who program but aren’t software developers. That’s a lot of people programming without seeing themselves as computer scientists or developers. Would even more program if they weren’t predisposed to think that computer science is so uninteresting?
That’s where I think the greatest impact of work like Performamatics might be — in changing the attitudes of the everyday citizens, improving their technical literacy, giving them greater understanding of the computing that permeates their lives, and keeping them open to the possibility that they might be part of that 13 million that needs to use programming in their careers. There will only be so many people who get CS degrees. There will be lots of others who will have attitudes about computing that will influence everything from federal investments to school board policies. It’s a large and important impact to influence those attitudes.
When I read this, I thought, “This is what Betsy DiSalvo’s been talking about, and why Glitch is so important.” It’s about creating a place where it’s okay to be black and want to learn computing. Amy Bruckman points out in her blog post that Glitch’s success is also about the money, that paying them made the task legitimate and convinced the Glitch students that high tech jobs really do pay well. (And I agree with Amy’s postscript: Betsy really is terrific, and your department ought to hire her.)
“Part of being a nerd is understanding, ‘Hey, you’re different,’” Lewis said. He went to grade school in private schools, which were predominantly white.
“There were very few black people there anyway,” he said. “But being the black guy who’s a geek made it weirder.
“I got a lot of ‘Hey, you’re not black, you’re white’ comments,” he said. “That actually really did bother me, and it still does.”
It was his ability to use computers and love of technology and anime that led classmates to tell him he was “white on the inside,” he said.
For Lewis, being a nerd became an identity crisis. He cites comedian Donald Glover: “It just recently became legal to be a black nerd.”