Talks and Trips: Learning Computing Concepts vs. Skills?

June 29, 2010 at 3:33 pm 3 comments

I’m writing from Chicago where I’m attending the International Conference of the Learning Sciences 2010. It’s pretty exciting for me to be back here. I helped co-chair the 1998 ICLS in Atlanta, but I haven’t been at this conference since 2002, when my focus shifted from general educational technology to specifically computing education. The theme this week is “Learning in the Disciplines.” I’m here at the invitation of Tom Moher to be part of a panel on Friday morning on computing education, with Yasmin Kafai, Ulrich Hoppe, and Sally Fincher. The questions for the panel are:

  • What specific type of knowledge is characteristic of computer science? Is there a specific epistemology?
  • Are there unique challenges or characteristics of learning in and teaching about computer science?
  • What does learning about computing look like for different audiences: young children, high school, undergraduate, and beyond (e.g., professional scientists, or professionals from non-computing disciplines)? In the case of “non-computing professionals,” what do they learn, and how do they learn it (e.g.,what information ecologies do they draw upon, and how do they find useful information)?
  • How do we support (broadly) learning about computer science?

In a couple weeks, I’m giving the keynote talk at the first AAI-10: The First Symposium on Educational Advances in Artificial Intelligence. I’m no AI person, but this conference has a strong computing education focus. I’m planning to use this as an opportunity to identifying challenges in computing education where I think AI researchers have a particularly strong lever for making things better. Not much travel for that one — I get to stay in Atlanta for a whole week!

In getting ready for my talk Friday, I’ve been trying to use themes from learning sciences to think about learning computing. For example, physics educators (BTW, Carl Weiman is here for the opening keynote tonight) have identified which physics concepts are particularly hard to understand. The challenge to learning those concepts is due in part to misconceptions that students have developed from years of trying to understand the physical world in their daily lives. I’ve realized that I don’t know about computing education research that’s looked at what’s hard about learning concepts in computing, rather than skills. We have lots of studies that have explored how students do (not?) learn how to program, such as in Mike McCracken’s, Ray Lister’s, and Allison Tew’s studies. But how about how well students learn concepts like:

  • “All information in a computer is made up of bytes, so any single byte could be anything from the red channel of a pixel in a picture, to an instruction to the processor.” Or
  • “All Internet traffic is made up of packets. So while it may seem like you have a continuous closed connection to your grandmother via Skype, you really don’t.”

Does anybody have any pointers to studies that have explored students learning conceptual (not skill-based) knowledge about computing?

I know that there is an argument says, “Computing is different from Physics because students have probably never seen low-level computer science before entering our classes, so they have few relevant preconceptions.” I believed that until I saw Mike Hewner’s data from his study of high school students in our Georgia Computes! mentoring program this last year. These are high school students who are being trained to be mentors in our younger student (e.g., middle school kids, Girl Scouts) workshops. They’re getting to see a lot of cool tools and learning a bunch about computer science. Mike found that they had persistent misconceptions about what computer science is, such as “Someone who is really great at Photoshop is a great computer scientist.” While that’s not a misconception about bytes or packets, that’s a misconception that influences what they think is relevant. The concept about bytes might seem relevant if students think that CS is all about great graphics design, but the packet concept interferes with their perception of Skype and doesn’t help with Photoshop — students might ignore or dismiss that, just as physics students say to themselves, “Yeah, in class and on exams, gravity pulls the projectile down, but I know that it’s really about air pressing down on the projectile.” So students’ misconceptions about what’s important about computing might be influencing what they pay attention to, even if they still know nothing about computer science.

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3 Comments Add your own

  • 1. Bradley Beth  |  June 30, 2010 at 12:19 pm

    Does anybody have any pointers to studies
    that have
    students learning conceptual (not skill-based) knowledge about

    I don’t know of any that are specific to computer science, but
    there are plenty in science/mathematics oriented research. It seems
    like CSE-specific research/theory doesn’t
    follow the general trends in education research that are evident in
    other fields. If you search for “constructivism” or “situated
    learning”, you get hits from Mordechai Ben-Ari,
    and that’s about it. “Novice/expert” searches give you more hits, most
    often because
    programming, specifically, is seen as a developing skillset.

    I guess my question is – why is that? I can think of a few

    CSE is a relatively new and esoteric field of research.

    CSE is “interrupt-driven” in the sense that studies are
    typically done to investigate
    a perceived problem (e.g., underrepresentation, poor enrollments),
    other disciplines have more in the way of exploratory research.

    CSE “inherits” from generalized math/science studies.

    The last point interests me the most. Does
    generalized science/math
    education research apply to computer science? Which is more harmonious
    – science or math?
    You mentioned in a later post
    that computer science is considered a science in Georgia, but is
    considered a math in Texas for high school graduation requirements. Is
    one more
    appropriate than the other?

    • 2. Mark Guzdial  |  June 30, 2010 at 12:34 pm

      Carl Weiman’s talk Tuesday night here at ICLS touched on this a bit for me. (BTW, Carl’s talk was completely different from his SIGCSE keynote, which really impressed me!) Carl talked about what was similar across disciplines in science education. One commonality across chemistry, biology, physics, etc. is that scientists are constantly comparing their models and theories to the real world — there’s an iterative checking process. Computer science is a science of the artificial. What do we check against? We’re bad at even checking that our software runs right! Thus, we’re much more like math — but as many have pointed out (in this blog, in Peter Denning’s column, etc.), we are also engineering-like since we make things — and engineering education is still at a fledgling state, too.

      I think that we don’t know how much we inherit yet. We’re still too new. Part of the problem here is that we in Computing don’t yet have a culture that seeks to make the education efforts better. That exists more strongly in the rest of science and math, but not so much in engineering either. It’ll develop, but I think it’s important for growth of the discipline-specific education research to have support of those in the discipline.

  • […] told Jennifer Turns of U. Washington about Mike Hewner’s recent studies, and she says that she sees similar issues among her engineering undergraduates.  She says that […]


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