Congratulations to Owen, Valerie, and Chris — ACM Award Winners!

Sharing Amber Settle’s note about ACM awardees from the computing education community, with her kind permission.

The SIGCSE Board would like to congratulate Owen Astrachan, Valerie Barr, and Chris Stephenson on their recent ACM awards.

Owen Astrachan was named recipient of the 2016 ACM Karl V. Karlstrom Outstanding Educator Award for three decades of innovative computer science pedagogy and inspirational community leadership in broadening the appeal of high school and college introductory computer science courses. His citation can be found here: http://awards.acm.org/award_winners/astrachan_3068814

Valerie Barr has received the 2016 Outstanding Contribution to ACM Award for reinventing ACM-W, increasing its effectiveness in supporting women in computing worldwide and encouraging participation in ACM.  Since becoming Chair of ACM-W in 2012, Barr has been a driving force in more than tripling the number of ACM-W chapters around the world. Her citation can be found here: http://awards.acm.org/award_winners/barr_3211646

Chris Stephenson, Head of Computer Science Education Programs at Google Inc., was recognized for creating the Computer Science Teachers Association, an international organization dedicated to supporting teachers and pursuing excellence in CS education for K-12 students. More information can be found here: http://awards.acm.org/about/2016-presidential-award-stephenson

Owen, Valerie, and Chris will receive their awards at the ACM Awards Banquet later this month in San Francisco. Please join us in congratulating them for their achievements.

Amber Settle

SIGCSE chair, 2016-2019

June 7, 2017 at 7:00 am 2 comments

Belief in the Geek Gene may be driven by Economics and Educational Inefficiency, plus using blocks to cross language boundaries

I visited China in the first part of May. I was at Peking University (PKU) in Beijing for a couple days, and then the ACM Celebration of the Turing Award in China (TURC) in Shanghai. I mentioned the trip in this earlier blog post. I wrote a blog post for CACM on a great panel at TURC. The panelists discussed the future of AI, and I asked about the implications for computing education. Are we moving to a future where we can’t explain to students the computing in their daily lives?

A highlight of my trip was spending a day with students and teachers at PKU. I taught a seminar with 30+ advanced undergraduates with Media Computation (essentially doing my TEDxGeorgiaTech talk live). It was great fun. I was surprised to learn that several of them had learned programming first in high school in Pascal. Pascal lives as a pedagogical programming language in China!

Perhaps the most striking part of my seminar with the undergraduates was how well the livecoding examples worked (e.g., I wrote and manipulated code as part of the talk).  All the PKU students knew Java, most knew C++, some knew Python — though I knew none of that when I was planning my talk. I wanted to use a tool that would cross programming language boundaries and be immediately understandable if you knew any programming languages. I used a blocks-based language.  I did my livecoding demonstration entirely in GP. I tested their knowledge, too, asking for predictions (as I do regularly, having read Eric Mazur’s work on predictions before demos) and explanations for those predictions.  They understood the code and what was going on. The funky sound and image effects cross language barriers.  Students laughed and oohed at the results.  Isn’t that remarkable that it worked, that I could give a livecoding demonstration in China and get evidence that the students understood it?

The most interesting session at PKU was talking with faculty interested in education about their classes and issues. I’ve always wondered what it’s like for students to learn programming when English is not their native language, and particularly, when the characters are very different. I asked, “Is it harder for your students to learn programming when the characters and words are all English?” The first faculty to speak up insisted that it really wasn’t an issue. “Our students start learning English at age 6!” said one. But then some of the other faculty spoke up, saying that it really was a problem, especially for younger students. In some middle schools, they are using Squeak with Chinese characters. They told me that there was at least one programming language designed to use Chinese characters, but the other faculty scoffed. “Yi is not a real programming language.” There was clearly some disagreement, and I didn’t follow all the nuances of the argument.

Then the Geek Gene came up in the conversation. One of the most senior faculty in the room talked about her challenges in teaching computer science. “Some students are just not suited to learning CS,” she told me. I countered with the evidence of researchers like Elizabeth Patitsas that there is no “Geek Gene.” I said, “We have no evidence that there are students who can’t learn programming.” She had an effective counter-argument.

“We do not have all the time in the world. We cannot learn everything in our lifetime. How much of a lifetime should a student spend learning programming? There are some students who cannot learn programming in the time available. It’s not worth it for them.”

I had not thought of the Geek Gene as being an economic issue. Her argument for the Geek Gene is not necessarily that students cannot learn programming. They may not be able to learn programming in the time available and using the methods we have available. This is not Geek Gene as only some students can learn to program. This is Geek Gene as economic limitation — we can’t teach everyone in the resources available.

I have an answer to that one. Want to reach more students? Either expand the time it will take to teach them, or use more effective methods!  This is the same response that I had offered to my colleague, as I described in an earlier blog post.

That insight gave me a whole new reason for doing our work in efficient CS education, like the greater efficiency in using subgoal-based instruction. The work of Paul Kirschner and Mike Lee & Andy Ko also emphasizes more CS learning in less time. If we can teach the same amount of CS in less time, then we can expand the number of students who can learn enough CS with a given amount of resource (typically, time). If we can’t convince teachers that there is no Geek Gene, maybe we can give them more effective and efficient teaching methods so that they see fewer students who don’t seem have the Geek Gene, i.e., who can learn enough CS in a single semester.

Below, evidence I was really at TURC

June 5, 2017 at 7:00 am 7 comments

We need a greater variety of CS teaching methods: The Way We Teach Math Is Holding Women Back

As I often do, I was trying to convince my colleagues that there is no “Geek Gene.”  One of them agreed that there is no Geek Gene.  But still…some people can’t learn CS, he insisted.  He pointed out that some people take a class “6-8 times to pass it.”

That got me thinking about the evidence he offered.  If someone takes the same course six times and can’t pass, does that mean that the student can’t learn CS?

Or maybe it proves that we’re insane, if Einstein’s famous quote is right (“Insanity: doing the same thing over and over again and expecting different results.”)  If the problem is our teaching and learning methods, simply repeating the exact same methods six times is not going to work.  Think about in terms of teaching reading.  We recognize that we need a variety of methods for teaching reading.  Having a dyslexic person take the exact same mainstream class six times will simply not work.

Why we are so resistant (as in the mathematics story linked below) to consider alternative teaching methods in CS?

The irony of the widespread emphasis on speed in math classrooms, with damaging timed tests given to students from an early age, is that some of the world’s most successful mathematicians describe themselves as slow thinkers. In his autobiography, Laurent Schwartz, winner of the world’s highest award in mathematics, described feeling “stupid” in school because he was a slow thinker. “I was always deeply uncertain about my own intellectual capacity; I thought I was unintelligent,” he wrote. “And it is true that I was, and still am, rather slow. I need time to seize things because I always need to understand them fully.”

When students struggle in speed-driven math classes, they often believe the problem lies within themselves, not realizing that fast-paced lecturing is a faulty teaching method. The students most likely to internalize the problem are women and students of color. This is one of the main reasons that these students choose not to go forward in mathematics and other STEM subjects, and likely why a study found that in 2011, 74% of the STEM workforce was male and 71% was white.

Source: Jo Boaler on Women in STEM, Ivanka Trump and Betsy DeVos – Motto

June 2, 2017 at 7:00 am 4 comments

How to be a great (CS) teacher from Andy Ko

Andy Ko from U-W is giving a talk to new faculty about how to be a great CS teacher.  I only quote three of his points below — I encourage you to read the whole list.  Andy’s talk could usefully add some of the points from Cynthia Lee’s list on how to create a more inclusive environment in CS.  CS is far less diverse than any other STEM discipline.  Being a great CS teacher means that you’re aware of that and take steps to improve diversity in CS.

My argument is as follows:

  • Despite widespread belief among CS faculty in a “geek gene”, everyone can learn computer science.
  • If students are failing a CS class, it’s because of one or more of the following: 1) they didn’t have the prior knowledge you expected them to have, 2) they aren’t sufficiently motivated by you or themselves, 3) your class lacks sufficient practice to help them learn what you’re teaching. Corollary: just because they’re passing you’re class doesn’t mean you’re doing a great job teaching: they may already know everything you’re teaching, they may be incredibly motivated, they may be finding other ways to practice you aren’t aware of, or they may be cheating.
  • To prevent failure, one must design deliberate practice, which consists of: 1) sustained motivation, 2) tasks that build on individual’s prior knowledge, 3) immediate personalized feedback on those tasks, and 4) repetition.

Source: How to be a great (CS) teacher – Bits and Behavior – Medium

May 29, 2017 at 7:00 am Leave a comment

Jean Sammet passes away at age 89

Jean Sammet passed away on May 21, 2017 at the age of 88. (Thanks to John Impagliazzo for passing on word on the SIGCSE-members list.)  Valerie Barr, who has been mentioned several times in this blog, was just named the first Jean E. Sammet chair of computer science at Mount Holyoke.  I never met Jean, but knew her from her work on the history of programming languages which are among the most fun CS books I own.

Sammet

GILLIAN: I remember my high school math teacher saying that an actuary was a stable, high-paying job. Did you view it that way?

JEAN: No. I was looking in The New York Times for jobs for women—when I tell younger people that the want ads were once separated by gender, they’re shocked—and actuary was one of the few listed that wasn’t housekeeping or nursing, so I went.Sammet found her way to Sperry. “Everything from there, for quite a while, was self-learned,” she says. “There were no books, courses, or conferences that I was aware of.” For her next move she applied to be an engineer at Sylvania Electric Products—though the job was again listed for men.

Source: Gillian Jacobs Interviews Computer Programmer Jean E. Sammet | Glamour

May 26, 2017 at 7:00 am 1 comment

How do we create cyberattack defenders?

 

Roger Schank (famous AI and cognitive science researcher, the guy who coined the term “learning sciences”) is putting his expertise to the task of creating cyberattack defenders.  The description of his process (linked below) is interesting.  It has all the hallmarks of his work — innovative, informed by research, driven by concrete tasks.  Notice the strong claim that I quoted below.  We shouldn’t be aiming for general cyber attack defense skills.  These skills are going to be industry-by-industry specific.  He’s directly informed by the research that suggests that these skills are unlikely to generalize.

One of the big questions is: where are we going to get the students?  How do we recruit students into this kind of program?

How can we help? The cyber attack course Socratic Arts is building for the DOD will be modified to make the projects specific to particular industries. The banks’ problems are obvious: hackers might want to steal money. Pharma’s problems are obvious: hackers might want to steal secrets. We intend to put out versions of our cyber attack course for each industry. These courses will take 6 months for a student to complete. We are not interested in giving an overview in the typical one week course that is no more than an intro. We want to train real cyber attackers who can help. The only way to learn is by practice (with advice). That’s how you learn to ride a bike and that’s how you learn to do anything.

Source: Cyber Attack Academy

May 24, 2017 at 7:00 am 1 comment

Survey to inform the next round of Computing Curricula

The Association for Computing Machinery (ACM) and the IEEE Computer Society (IEEE-CS) with support from other organizations are producing a new curricular report titled, “Computing Curricula 2020: An Overview Report” (CC2020) in an effort to retain global currency in the computing curricula guidelines.  We reach out to you because we value your opinion in this effort. We invite you to participate in this project by responding to a brief survey found at the URL
 
https://www.surveymonkey.com/r/W6W76LB
 
where you can provide your comments by responding to the survey prompts.  The survey should take between 3 and 5 minutes, we do apologize for any cross postings
 
Thank you in advance for your time and valuable contributions to this project.
 
—The CC2020 Task Force

May 22, 2017 at 7:00 am Leave a comment

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