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.
I predict that if we did this study with CS teachers, we’d find the same result. The belief that CS is for males and not for females is deeply ingrained in the perceptions of our field. Kahneman would tell us that it’s part of our System 1 thinking (see NYTimes Book Review). What do you think teachers would draw if asked to “draw a computer scientist“? I predict that the gender bias that favors males as computer scientists would be greater for post-secondary teachers than for secondary or elementary teachers. Most secondary school CS teachers that I’ve met are sensitive to issues of gender diversity in computing, and they actively encourage their female students. Most post-secondary CS teachers with whom I’ve worked are not sensitized to issues of women in computing and have not changed how they teach to improve gender diversity (see example here).
In the study, teachers graded the math tests of 11-year-olds and, on average, the scores were lower for girls. But, when different teachers graded the same tests anonymously, the girls performed far better (out-performing the boys in many cases.)
Dr. Edith Sand, one of the researchers, told American Friends of Tel Aviv University, that the issue wasn’t overt and obvious sexism, but “unconscious discouragement.”
The study goes on to say that the gender biases held by elementary school teachers have an “asymmetric effect” on their students — the boys’ performance benefits and girls’ performance suffers based on the teacher’s biases. Boys do well because teachers believe they will, girls don’t because teachers believe they won’t.
Interesting perspective from a blogger in the Scratch community, liked below. I do frequently hear the pattern described in the post quoted below. “I’ve started by daughter/niece/local-school on Scratch, and now I want to know how to move them into something ‘real’ (e.g., text).” I typically point them to amazing things that can be done in Scratch (like Alex Ruthmann’s beautiful livecoding of music in Scratch).
I recently got a chance to play with GP, a new programming language from John Maloney (of Scratch fame), where all blocks and texts are isomorphic. There’s a slider that lets you switch from one to the other. Even the debugger and class browser show up with tiles. Where does that play out in this debate? GP is the first blocks-based language I’ve used with the right primitives to do MediaComp, so I built one of my examples in it. Took me about three times as much time to write and four times as much space (in screen real estate) as in Python (even with John looking over my shoulder guiding me). Maybe that’s not a bad thing — maybe that encourages a different style of use. Next time I try something like that, I’m far more likely to think about building my own blocks and using more abstraction to save on dragging-and-fitting effort.
I’ve been a part of the Scratch community for about 8 years now (yes, really). During this time, I’ve noticed a pattern that seems to apply to a lot of people:
join Scratch => create projects => discover text-based programming => quit Scratch because of “real programming”
Note the scare quotes around “real programming”. Generally, a “real” programming language is text-based (C, Python, etc.) and apparently qualifies as real because it’s used by well-known developers for something.
Obviously I disagree with disqualifying Scratch as a real programming language.
I like the recognition of the importance of learning to code in this piece, but not the sense of privilege around it. “Even” people who get into incredibly expensive schools and want to focus on “ideas” should learn to code. It’s not really beneath you to learn to code, the author is telling us. Even the elites should! Computing for all!
It’s tempting but irresponsible to say students should teach themselves about venture capital firms, iOS, UI/UX and product design. When students can’t find the 25th hour in their days to do so, most will choose to focus on their (reinvent-the-wheel) classes. As ex-Snapchat COO Emily White says, “Our education system tends to train kids to be right rather than to learn.” This isn’t okay when we need more engineers in Silicon Valley.
We must not neglect the merits of technical skills in the conception of the “idea person.” What the 60-year old entrepreneur and others of his generation—the people in control of the education we receive—don’t realize is this: For college students dreaming of becoming unicorns in Silicon Valley, being an “idea person” is not liberating at all. Being able to design and develop is liberating because that lets you make stuff.
Source of the “Geek Gene”? Teacher beliefs: Reading on Lijun Ni, Learning from Helenrose Fives on teacher self-efficacy
I discovered the below quoted post when I was looking up a paper by my former student, Lijun Ni. It’s nice to see her work getting recognized and reviewed! I talked a lot about her work when I was talking to PhD students at the University of Oldenburg program — Lijun has studied the beliefs of CS teachers, and that’s super important.
One of the other international guests at the Oldenburg program I attended last month (see post here) was Helenrose Fives who has literally written the book on teacher beliefs (see Amazon reference). Several of the PhD students who presented their research talked about student teachers having lower self-efficacy after actually being in the classroom, less commitment to ideals like inquiry learning, and less belief that students can learn. Helenrose said that that’s really quite common. Teachers have a high level of self-efficacy (“I can teach using novel approaches that will really help students learn!”) before they enter the classroom, and that sense of self-efficacy falls off a cliff once they face the reality of the classroom. The self-efficacy rises over time (up and down, but mostly up) but never reaches the optimism of before teachers enter the classroom.
I talked to Helenrose about what her work means for University CS teachers. In general, the work she describes is about school teachers, not faculty. She agreed that it’s possible for University CS teachers to have high self-efficacy even if they are not successful teachers, because University teachers define self-efficacy differently than school teachers. School teachers are responsible for student learning. They know individual students. They actually know if they are successful in their teaching or not (in terms of student learning and engagement). University teachers tend to have larger classes, and they tend to teach via lecture. They usually have little knowledge of individual student learning and engagement. Their sense of self-efficacy may arise from their ability to succeed at their task, “I can give great lectures. (Almost nobody falls asleep.) I can manage huge classes.” Where they do have knowledge of learning and evidence of ineffective teaching, they may simply decide that it’s the student’s fault. Perhaps this is where the Geek Gene is born.
Here’s a hypothesis: If a University teacher has high self-efficacy (great confidence in his or her teaching ability) and sees evidence of students not learning, it’s rational for that teacher to believe that the problem lies with the students and that the problem is innate — beyond the ability of the teacher to improve it.
In the first study, Ni interviewed teachers about their identity in order to establish what strengths and weaknesses are common in high school computer science teachers. She found that the teaching identity of computer science teachers is largely underdeveloped compared to teachers in other fields, and that often computer science teachers prefer to identify as a math teacher or a business teacher, rather than a computer science teacher.
Further, she found that high school computer science teachers generally do not have any sort of teaching support community to turn to, because they are often the only computer science teacher at their school.
All of these problems combine to keep computer science teachers from developing a strong teaching identity centered in the computer science field. Instead, we have teachers with low commitment levels to the field training our next generation of programmers in basic computing skills that are generally unrelated to the field of computer science itself.