Posts tagged ‘computing education’

Take the Computer Scientists’ Oath

My colleague Beth Mynatt (on the right above) gave a commencement address at one of her alma maters, and came up with the great idea of a Computer Scientists Oath.  I’m quoting it below in its entirely, but recommend the article for more context on her oath and her speech.  I am teaching ethics (“CS4001 Computers and Society”) for the first time this summer in Barcelona. I hadn’t realized that most students in my classes haven’t thought about these issues — I didn’t realize the bubble in which I live (e.g., Human-Centered Computing PhD program, a School of Interactive Computing) where we do talk about such things. I agree with Beth for the need of such an oath, and I’m proud of her efforts to create one.  Barbara Ericson sent me an Engineering Oath that is particularly aimed at Facebook and other Silicon Valley engineers — some of these ideas could influence a future Computer Scientists Oath.

During her remarks, Mynatt lamented that the field of computer science lacks a professional oath to bind them together at this important moment as they embark on their careers. In the spirit of the medical field’s Hippocratic oath, she challenged the graduates to join her and take the following oath, as a formal recognition of the importance of our field to the wellbeing of society, and our collective responsibility to fulfill our obligations:

Today, I join the ranks of computer scientists worldwide. 

I will remember that I remain a member of society, with special obligations to all my fellow human beings.

I will design and build computing systems that enhance the quality of daily life for individuals and for society.

I will protect the dignity of users and others affected by computing systems, respecting the diversity of all cultures, and safeguarding against threats to health and safety.

I will respect the privacy and rights of all people and recognize the special role I have in judiciously collecting, storing and using their information, and creating systems that aim to shape their behavior.

I will work for fair wages; honorably guarding my reputation and my colleagues in our work practices, while respecting the intellectual contributions of others.I will improve the public understanding of computing and its consequences.

May I always act so, as to preserve the finest traditions of my field, and may I long experience the joy of inventing the future through my endeavors.

Source: Department of Computer Science at North Carolina State University

June 9, 2017 at 7:00 am 7 comments

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:

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:

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:

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

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

Stanford CS department updates introductory courses: Java is Gone

Stanford has decided to move away from Java in their intro courses. Surprisingly, they have decided to move to JavaScript.  Philip Guo showed that most top CS departments are moving to Python.  The Stanford Daily article linked below doesn’t address any other languages considered.

The SIGCSE-Members list recently polled all of their members to talk about what they’re currently teaching.  The final spreadsheet of results is here.  Python appears 60 times, C++ 54 times, Java 84 times, and JavaScript 28 times.  I was surprised to see how common C++ is, and if Java is dying (or “showing its age,” as Eric Roberts is quoted below), it’s going out as the reigning champ.

When Java came out in 1995, the computer science faculty was excited to transition to the new language. Roberts wrote the textbooks, worked with other faculty members to restructure the course and assignments and introduced Java at Stanford in 2002. “Java had stabilized,” Roberts said. “It was clear that many universities were going in that direction. It’s 2017 now, and Java is showing its age.” According to Roberts, Java was intended early on as “the language of the Internet”. But now, more than a decade after the transition to Java, Javascript has taken its place as a web language.

Source: CS department updates introductory courses | Stanford Daily

ADDENDUM: As you see from Nick Parlante’s comment below, the JavaScript version is only an experiment.  From people I’ve talked to at Stanford, and from how I read the article quoted above (“more than a decade after the transition to Java, Javascript has taken its place”), I believe that Stanford is ending Java in CS106.  I’m leaving the title as-is for now. I’ve offered to Marty Stepp that if CS106 is still predominantly Java in one year, I will post a new blog post admitting that I was wrong.  Someone remind me in April 2018, please.

April 21, 2017 at 7:09 am 33 comments

The Limitations of Computational Thinking: NYTimes

The New York Times ran a pair of articles on computing education yesterday, one on Computational Thinking (linked above and quoted below) and one on the new AP CS Principles exam.  Shriram and I are quoted as offering a more curmudgeonly view on computational thinking.  (Yes, I fixed the name of my institution in the below quote, from what how it is phrased in the actual article.)

Despite his chosen field, Dr. Krishnamurthi worries about the current cultural tendency to view computer science knowledge as supreme, better than that gained in other fields. Right now, he said, “we are just overly intoxicated with computer science.”

It is certainly worth wondering if some applications of computational thinking are trivial, unnecessary or a Stepford Wife-like abdication of devilishly random judgment.

Alexander Torres, a senior majoring in English at Stanford, has noted how the campus’s proximity to Google has lured all but the rare student to computer science courses. He’s a holdout. But “I don’t see myself as having skills missing,” he said. In earning his degree he has practiced critical thinking, problem solving, analysis and making logical arguments. “When you are analyzing a Dickinson or Whitman or Melville, you have to unpack that language and synthesize it back.”

There is no reliable research showing that computing makes one more creative or more able to problem-solve. It won’t make you better at something unless that something is explicitly taught, said Mark Guzdial, a professor in the School of Interactive Computing at Georgia Tech who studies computing in education. “You can’t prove a negative,” he said, but in decades of research no one has found that skills automatically transfer.

April 5, 2017 at 7:00 am 6 comments

Weapons of Math Destruction: invisible, ubiquitous algorithms are ruining millions of lives

C.P. Snow got it right in 1961. Algorithms control our lives, and those who don’t know what algorithms are don’t know what questions to ask about them.  This is a powerful argument for universal computing education.  I like the below quote for highlighting that a better term for the concern is “model,” not “algorithm.”

Discussions about big data’s role in our society tends to focus on algorithms, but the algorithms for handling giant data sets are all well understood and work well. The real issue isn’t algorithms, it’s models. Models are what you get when you feed data to an algorithm and ask it to make predictions. As O’Neil puts it, “Models are opinions embedded in mathematics.”

Source: Weapons of Math Destruction: invisible, ubiquitous algorithms are ruining millions of lives / Boing Boing

March 27, 2017 at 7:00 am Leave a comment

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