The world is about more than computing. It’s easy for those of us who live and work in CS to see it as CS-centric. I work in a section of Atlanta that is bursting with high-tech startups. I found this article compelling — not because it threw cold water on the vision of Atlanta as a “Silicon Valley East,” but because it painted a picture of how much more diverse the economy in Atlanta really is.
In reality, metro Atlanta’s relationship with the tech sector is, well, complicated.
Georgia boasts about 280,000 tech jobs, according to Technology Association of Georgia president and chief executive officer Tino Mantella — the great majority of them in metro Atlanta. But information technology jobs only make up about 3.5 percent of the area’s labor market, down from a peak of 4.7 percent in the 1990s, federal Bureau of Labor data shows.
And California, home to the real Silicon Valley, dominates venture capital investing — the lifeblood of tech startups — with 56 percent of spending compared to the 1 percent in Georgia, Mantella said.
I’m currently reading Nobel laureate Daniel Kahneman’s book, “Thinking Fast, Thinking Slow” (see here for the NYTimes book review). It’s certainly one of the best books I’ve ever read on behavioral economics, and maybe just the best book I’ve ever read about psychology in general.
One of the central ideas of the book is our tendency to believe “WYSIATI”—What You See Is All There Is. Kahneman’s research suggests that we have two mental systems: System 1 does immediate, intuitive responses to the world around us. System 2 does thoughtful, analytical responses. System 1 aims to generate confidence. It constructs a story about the world given what information that exists. And that confidence leads us astray. It keeps System 2 from asking, “What am I missing?” As Kahneman says in the interview linked below, “Well, the main point that I make is that confidence is a feeling, it is not a judgment.”
It’s easy to believe that University CS education in the United States is in terrific shape. Our students get jobs — multiple job offers each. Our graduates and their employers seem to be happy. What’s so wrong with what’s going on? I see computation as a literacy. I wonder, “Why is our illiteracy rate so high? Why do so few people learn about computing? Why do so many flunk out, drop out, or find it so traumatic that they never want to have anything to do with computing again? Why are the computing literate primarily white or Asian, male, and financially well-off compared to most?”
Many teachers (like the comment thread after this post) argue for the state of computing education based on what they see in their classes. We introduce tools or practices and determine whether they “work” or are “easy” based on little evidence, often just discussion with the top students (as Davide Fossati and I found). If we’re going to make computing education work for everyone, we have to ask, “What aren’t we seeing?” We’re going to feel confident about what we do see — that’s what System 1 does for us. How do we see the people who aren’t succeeding with our methods? How do we see the students who won’t even walk in the door because of how or what we teach? That’s why it’s important to use empirical evidence when making educational choices. What we see is not all there is.
But, System 1 can sometimes lead us astray when it’s unchecked by System 2. For example, you write about a concept called “WYSIATI”—What You See Is All There Is. What does that mean, and how does it relate to System 1 and System 2?
System 1 is a storyteller. It tells the best stories that it can from the information available, even when the information is sparse or unreliable. And that makes stories that are based on very different qualities of evidence equally compelling. Our measure of how “good” a story is—how confident we are in its accuracy—is not an evaluation of the reliability of the evidence and its quality, it’s a measure of the coherence of the story.
People are designed to tell the best story possible. So WYSIATI means that we use the information we have as if it is the only information. We don’t spend much time saying, “Well, there is much we don’t know.” We make do with what we do know. And that concept is very central to the functioning of our mind.
I believe the result described in the article below, that a critical limitation of teacher’s ability to use technology is too little understanding of technology. In a sense, this is another example of the productivity costs of a lack of ubiquitous computing literacy (see my call for a study of the productivity costs). We spend a lot on technology in schools. If teachers learned more about computing, they could use it more effectively.
In 2010, for example, researchers Peggy A. Ertmer of Purdue University, in West Lafayette, Ind., and Anne T. Ottenbreit-Leftwich of Indiana University, in Bloomington, took a comprehensive look at how teachers’ knowledge, confidence, and belief systems interact with school culture to shape the ways in which teachers integrate technology into their classrooms.
One big issue: Many teachers lack an understanding of how educational technology works.
But the greater challenge, the researchers wrote, is in expanding teachers’ knowledge of new instructional practices that will allow them to select and use the right technology, in the right way, with the right students, for the right purpose.
Australia may become the next country to teach computing in all schools, if a Labor Government gets elected. I hope that, if it happens, it’s done well. It’s expensive to get real CS education into every school. It’s cheap and easy to declare that any course that teaches how to use software is “CS.”
Bill Shorten’s recent announcement that, if elected, a Labor Government would “ensure that computer coding is taught in every primary and secondary school in Australia” has brought attention to an increasing world trend.
There is merit in school students learning coding. We live in a digital world where computer programs underlie everything from business, marketing, aviation, science and medicine, to name several disciplines. During a recent presentation at a radio station, one of our hosts said that IT would have been better background for his career in radio than journalism.
There is also a strong case to be made that Australia’s future prosperity will depend on delivering advanced services and digital technology, and that programming will be essential to this end. Computer programs and software are known to be a strong driver of productivity improvements in many fields.
Maria Klawe gets a lot of attention for promoting women in CS at Harvey Mudd College, but she’s the College President. Closer to the on-the-ground action is Ran Libeskind-Hadas who is the CS Department Chair there. In the post below, he lays out the argument for everyone taking CS in College.
I’m encouraged by an increasing number of innovative introductory courses that provide students with these rich experiences. And, I’m very excited to see students voting for these courses with their feet. At my institution, Harvey Mudd College, we developed a set of introductory courses that are not only required for all Harvey Mudd students but are now immensely popular among non-majors at our four sister institutions in the Claremont Colleges consortium. At a college of 800 students, we are teaching introductory computer science to all of our first-year students, regardless of their ultimate major. And, we are attracting hundreds of students each from our sister colleges in Claremont. They are literature, economics, and sociology majors – among many others. And Harvey Mudd does not have a monopoly on innovative introductory courses. A number of other institutions including the University of Washington, Harvard, and others have pioneered their own successful courses in a similar spirit.
I wrote that blog post because we really have had a long debate in our faculty email list about many of those topics. I recently saw our Dean at an event, and he told me that he hadn’t read the thread yet (but he planned to) because “it must be 100 messages long.” Most of the references in that blog post came from messages that I wrote in response to that thread. It was a long post because people generally didn’t agree with me. Several senior, well-established (much more famous than me) faculty strongly disagreed with the evidence-based argument I was making. The thread finally ended when one of the most senior, most respected faculty in the College wrote a note saying (paraphrased), “There are probably better teaching evaluation methods than the ones we now use. I’m sure that Mark knows teaching methods that would help the rest of us teach better.” And that was it. Thread ended. The research-based evidence that I offered was worth fighting about. The word of authority was not.
I’ll bet that faculty across disciplines similarly respond to authority more than evidence. We certainly see the role of authority in Physics Education Research (PER). Pioneering PER researchers were not given much respect and many were ostracized from their departments. Until Eric Mazur at Harvard had his students fail the Force Concept Inventory (FCI), and he changed how he taught because of it. Until Nobel laureate Carl Wieman decided to back PER (all the way to the Office of Science Technology and Policy in the White House). Today, the vast majority of physics teachers know research-based teaching methods (even if they don’t always use them). FCI existed before Mazur started using it, but it really started getting used after Mazur’s support. The evidence of FCI didn’t change physics teaching. The voice of authority did.
While we might wish that CS faculty would respond more to evidence than authority (see previous post on this theme), this insight suggests a path forward. If we want CS faculty to improve their teaching and adopt evidence-based practices, top-down encouragement can have large impact. Well-known faculty at top institutions publicly adopting these practices, and Deans and Chairs promoting these practices can help to convince faculty to change.
I’ve written a couple times now about the workshop I attended at the University of Oldenburg the first week of June. (See the post where I talked about my two weeks in Germany.) For Blog@CACM, I wrote a post about teaching as collective practice and the workshop I took with Barbara Hofer (see post here). I wrote about learning about teacher beliefs and self-efficacy from Helenrose Fives here (see post).
Before we left for the workshop, I got to spend time with Ira Diethelm at the University of Oldenburg and one of her students. Ira is one of at least 16 (that Ira could count) CS Education professors in Germany. Ira works with pre-service teachers, in-service teachers, and graduate students. Her graduate students build outreach efforts and curricula as part of their research, then roll them out and provide resources to teachers. It’s remarkable what Ira is doing, and I understand that the other German CS Ed professors do similar things. I came away with a new insight: If we want to bootstrap and sustain CS Education in the United States, we should fund several endowed chairs of CS Education at top Schools of Education. Eventually, we have to have pre-service computing education programs if we want to make CS education sustainable (see that post here). Creating these endowed chairs gives us the opportunity to create positions like Ira’s in the United States.
Overall, the workshop was a terrific experience. The PhD student work was fascinating, and I enjoyed discussing their research with them. It was great to hear about German research perspectives that I hadn’t previously, like the Model of Educational Reconstruction that informs science education (see paper here). Barbara and Helenrose were only two of a several outstanding international education researchers who attended. As I mentioned to Pat Alexander (who has a length Wikipedia page of her accomplishments), I enjoyed being able to wallow in educational psychology for a week, because I so rarely get to do that. I gave a talk on three of our projects related to the theme of developing teachers: on Lijun Ni’s work on teacher identity, on the Disciplinary Commons for Computing Education, and on our ebook for preparing CS teachers. (See Slideshare here.)
The response to my talk was fascinating. Some of the German mathematics education researchers are deeply opposed to computing education in schools. (I suspect that more than one of them completely skipped my talk because they are so opposed.) “Computing education keeps stealing from mathematics teachers, and learning mathematics is more important.” At my talk, Pat Alexander asked me the same question that Peter Elias asked Alan Perlis in 1961, “Won’t the computer eventually just understand us? Doesn’t the computer just become invisible and not need to be programmed?” I told the story about Alan Perlis’s talk and about Michael Mateas’s argument, “There will always be friction.” From the computing educators, I heard a lot of anger. The German computing education researchers feel that other fields squeeze CS out because the they are not willing to allow computing education to take up any time or budget in the curriculum.
Probably the most interesting pushback was against computational thinking. The educational psychologists thought it was unbelievable that learning computing would in any way impact the way that people think or problem-solve in everyday life. “Didn’t we believe that once about Latin? and Geometry?” asked Gavin Brown. The psychologists at the workshop I attended saw a clear argument that we need to introduce computing in high school so that students can see if it’s for them, but not to teach general problem-solving skills. If we really want algorithmic thinking, they can design easier ways to achieve that goal than teaching programming.
We can probably help students to learn about computing in such a way that it might influence problem-solving on the computer. That’s part of Jeanette Wing’s model of Computational Thinking (see her 2010 paper). It’s the “Computational Thinking in Daily Life” part that the psychologists weren’t buying. That learning about computation helps with computational X is quite reasonable. If you understand what IP addresses are, we can help you to understand DNS problems and to realize that it’s not really that big of a deal if Wikipedia stores your IP address (see story about Erika Poole’s research). There is evidence that learning one programming language will likely transfer to another one (see Michal Armoni’s paper on transfer from Scratch to a text-based language). Learning to program is unlikely to influence any problem-solving in everyday life.