Anyone Can Learn Programming: Teaching > Genetics

Mark Guzdial
Georgia Institute of Technology professor Mark Guzdial

The SIGCSE-members list recently had a discussion about whether students are born with programming ability (the so-called “Geek Gene”), or if effort leads to greater programming ability. As one correspondent put it:

Over the years, I have come to believe that those of us who can become successful programmers have different internal wiring than most in the population.

This is one form of the “Geek Gene” hypothesis — that students are either born with the ability to program, or they’re not. Teaching doesn’t have much to do with it. Raymond Lister has a thought a lot about the “Geek Gene” hypothesis as described in an Inroads article, presented at ICER 2013, and studied in his award-winning paper (with co-authors Porter and Zingaro). In general, Raymond doesn’t see a reason to believe in the “Geek Gene.”

The “Geek Gene” hypothesis is widely held among computer science faculty. Clayton Lewis found in a survey in 2007 (see article here) that CS faculty who responded strongly disagree (77%) with the sentence: “Nearly everyone is capable of succeeding in the computer science curriculum if they work at it.” The CS students surveyed were far more likely to agree with the statement than the CS faculty!

In educational psychology terms, those that hold to the “Geek Gene” hypothesis have a fixed mindset, as opposed to a growth mindset. These are terms used by Carol Dweck to describe how students approach their studies. (See her webpage here, and a relevant Wikipedia article here.) Students with a fixed mindset believe that their abilities are fixed from birth. It’s a talent that either you got or you don’t. If fixed mindset students do badly on a test, they chalk it up to fate. “Guess I’m just not good at that — have to find a new field that I’m good at.” A growth mindset student believes that dedication and hard work can lead to greater success. A bad grade leads to the reaction, “I have to work harder next time.” There is significant evidence that a growth mindset leads to greater effort and achievement.

Fortunately, there are studies that show that we can teach a growth mindset. We can help students change from a fixed to a growth mindset. In fact, Quintin Cutts and his colleagues showed a successful intervention that improved computing students’ performance by encouraging a growth mindset (paper here). But that kind of intervention is not possible if the teachers themselves have the fixed mindset.

The most dangerous part of the “Geek Gene” hypothesis is that it gives us a reason to stop working at broadening participation in computing. If people are wired to program, then those who are programming have that wiring, and those who don’t program must not have that wiring. Belief in the “Geek Gene” makes it easy to ignore female students and those from under-represented minority groups as simply having the wrong genes. The problem is that those who seem like they have a “Geek Gene” or who are well-prepared are typically the students who have had privileges, who have had the opportunity to develop ability in computing before they enter our classroom. We need to teach as if anyone can learn to program in order to broaden participation in computing and develop a more diverse computing community and workforce.

The notion that we can develop expertise through dedication and deliberate practice is at the core of the growth mindset. A recent Slate article (available here) argued, “Practice doesn’t make perfect.” The Slate authors claim that practice doesn’t explain enough of the variance in expertise, so they conclude that it is likely genetic/innate differences that explain expertise. I’ve just written a blog post (see here) explaining why the claims of the Slate article and the meta-analysis behind it are mistaken. Nobody says that just practice leads to expertise. Challenging practice and practice with a teacher is far more effective than just practice. What we’re calling genetic or innate differences is far more likely to be due to experiences before the classroom. If you believe that writing out the instructions to making a peanut butter and jelly sandwich (as in this exercise) can help develop computing expertise, why wouldn’t you believe that kids who (for example) write out more instructions for others or explain more things to others before coming into our classes might have a headstart on developing expertise?

From our perspective as teachers, it shouldn’t matter why a student is less-prepared than others in the class. We can help all students improve. Deliberate practice leads to expertise, and deliberate practice is facilitated by good teaching.

The belief that students are born or have “different internal wiring” to be a programmer ignores the value of the teacher. A 2012 OECD report put the point in two words: “Teachers Matter.” Hours of practice with a good teacher are far more likely to contribute to expertise than hours of practice alone. Rather than worry about our students’ genetics, we should be thinking about how to make ourselves more effective as teachers.

Teaching is not an innate skill. I guarantee that I can help novices learn programming far better hour-for-hour than if the novices worked on their own or via a self-study service like Khan Academy or Code Academy. I don’t have special teaching genes. I am comfortable making that claim because I have worked hard at learning what computing students typically get wrong, how to diagnose the problems, and how to orchestrate learning situations where they develop more robust understanding. We can learn to be better teachers.

Just how much can good teaching overcome genetic/innate differences or differences due to prior background? Here’s a research question that is far more important than if P==NP. Is Teaching > Genetics? Even if programming has genetic/innate roots, how can we teach to get beyond those? Even if students have prior backgrounds that make them less prepared for our classes, how can we teach to bring them up to speed?

We can develop teaching strategies that get past problems rooted in genetics. There are probably limits where good teaching can’t help, but we don’t know those limits yet. We have some wonderful success stories:

  • Men tend to be better than women at spatial reasoning. We don’t know if the difference is genetic/innate. However, researchers at the Spatial Intelligence and Learning Center have shown that they can teach women to be as good as men at visual ability, and the ability transfers to new contexts and persists long after training (see journal article here and a great overview piece here.)
  • Subitizing is the ability to group numbers and see patterns in those numbers. If kindergarteners don’t subtilize in groups of 5 or 10, they may be developmentally disabled in mathematics. However, we can diagnose a lack of subtilizing in kindergarten (see this review article) and can provide learning opportunities to address it (see example materials here).

Programming is certainly a malleable cognitive skill, like spatial reasoning and subitizing. So, even if you believe that programming is a “wired” ability, or if you get groups of students in your classes who aren’t prepared, there’s every reason to believe that you can still teach them to program better than they could when they enter your class.

The critical issue is that we in computing education need to take seriously the development of teaching techniques, the research on the most effective teaching techniques, and the sharing of those findings. We can help students overcome difficulties or lack of preparation. We can help students develop growth mindsets. We can improve the programming expertise of any student who comes into our classes.

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