How computing and physics learning differ

April 1, 2010 at 3:44 pm 11 comments

Allison Elliott Tew successfully defended her thesis proposal this morning. Hooray!  You may recall from my pre-SIGCSE description of her work, that she’s attempting to build a language independent measure of CS1 learning.  Allison talked about concept inventories in a way this morning that I found intriguing with respect to computing.

You may know that concept inventories are used to assess student knowledge about an area.  They are based on an analysis of what students already think about an area, and those pre-conceptions/misconceptions appear as “distractors” in the multiple-choice questions.  The most famous of these assessments is probably the Force Concept Inventory, which was developed over 25 years by David Hestenes.  The FCI measures knowledge about Newtonian mechanics, and it includes all those deeply-held beliefs that students have about the world from living in it for 18 years before entering a College physics classroom.  The FCI was used in a huge study (n>6000 students) by Richard Hake to show that instruction alone was ineffective in shaking those beliefs, and “interactive engagement” (like peer instruction) was necessary to get students to learn physics well.

There are efforts to build concept inventories for computer science, but they run into a problem when creating a direct mapping.  Students enter our classes, for the most part, without any conceptions at all of computing. If they have conceptions (or misconceptions), they’ve only developed them recently.  Students may have an idea about how computers work from years of working with those computers, but the challenging issues of variable types, defining classes, pointers, recursion, and essentially everything that students have trouble with in computing are all totally new to their computer science classes. They don’t have 18 years to develop preconceptions and misconceptions.  That makes it hard to develop a CS concept inventory, because any wrongly-held beliefs that students have are due to their instruction, not due to naive reflection on experience.

Which leads me to my question: How deeply held are those misconceptions?  Physics misconceptions are well-documented and very hard to shake.  They have served 18 year olds very well!  How about computer science misconceptions?  Since they form over a short period of time, can we just correct them with, “No, that’s wrong”?  Maybe if we taught things better, there would be no misconceptions to inventory.  And if there are some, maybe they’re really easy to change.  I don’t know how one would measure strength of misconception, but I’ll bet that it’s different between physics and computer science.

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Isn’t this what computers are for? How to teach teachers

11 Comments Add your own

  • 1. Raymond Lister  |  April 1, 2010 at 4:22 pm

    I agree that the strength of misconceptions (and their centrality to teaching) is probably different between physics and CS. (Naturally, this is something I was forces to grapple with in my time at UBC, given the importance of the physics education literature in the Carl Weiman Science Education Initiative.)

    I think we could relatively easilty reduce the problem of misconceptions in programming if we reduced the emphasis on students writing novel code, and increasd the emphasis on students reading code. Its hard to maintain a misconception when, for example, it leads you to an incorrect trace of some code (and your incorrect trace is detected as part of formative or summative assessment).

    (Okay. After my flurry of postings in the last 24 hours, I think I’ll take a vow of silence for a while.)

    Reply
  • 2. Doug Holton  |  April 2, 2010 at 4:36 pm

    Computer science is usually more of an engineering/design field. Wendy Newstetter has written about misconceptions about design: http://dx.doi.org/10.1145/305786.305847

    But you might be interested in checking out FASCI. It gives science teachers various scenarios and asks them what they would do.
    https://webfiles.colorado.edu/talbotr/www/FASCI.html

    Reply
    • 3. Mark Guzdial  |  April 3, 2010 at 10:10 am

      Mike Hewner just completed a study of high school students and their definitions of computing, and Wendy’s paper (and Mike McCracken’s, lead author on that paper) may be very relevant. While students don’t have long-held beliefs about details like pointers and class definitions, they do have deeply held beliefs about use of computing (as Mark U-L points out). If “computer science” is the ability with Maya to produce Pixar-level animations and with PhotoShop to produce magazine-quality cover shots, then what is this academic stuff we’re teaching? Does computer science play any role in the design and creation of what students recognize as computing? If not, then it’s irrelevant and easily ignored. That may be the important misconception, deeply held and built over many years, that we need to address.

      Reply
  • 4. Mark Urban-Lurain  |  April 2, 2010 at 4:40 pm

    Interesting take on the differences between physics and CS. Aristotelean concepts of force are the results of lived experience in the world and they work for navigating through the world, so are very persistent. I am not sure I agree with the perspective that students haven’t got misconceptions. Particularly current students who have grown up with computers (just like they grew up running around in the physical world) so they have mental models/schemas of these systems. They may not have been formally taught computing, but misconceptions are not only the result of teaching (though many are).

    On the other hand, most of us older folks (e.g., many faculty) did not have exposure to computers until we probably took a computing class. In my case, my first “hands on” experience was as a freshman doing fortran on punch cards (not really hands on 🙂 However, my interactions with the computer were specifically structured with the goal of learning programming.

    Reply
  • 5. Doug Holton  |  April 2, 2010 at 5:03 pm

    I forgot to mention, the most useful information is when you give a concept inventory at the end of a course (or degree), to see what misconceptions they still have. It’s not all new to them by then, but they may have developed common patterns of errors or misconceptions.

    In electrical engineering/circuits, for example, many students mix up high pass and low pass filters. Or they may have memorized formulas for capacitors and inductors and so forth, but never learned what those components actually do. Our circuits concept inventory we mainly give just as a post-test to see what concepts they are still have problems with.

    Reply
  • 6. Jeff Graham  |  April 3, 2010 at 3:09 pm

    One of my colleagues and I have been thinking about the fact that we start students programming experience with an abstract construction (a programming language) and a high level one at that. We don’t start teaching math to children by giving them functions and teaching them the calculus or doing proofs. Maybe the problem with teaching programming is that we start too high up the chain. They have no concepts wrong or otherwise and so there is nothing to build on. In my irrational moments, I sometimes think we should start with assembly language programming, still an abstraction, but a little closer to home.

    Reply
    • 7. Mark Guzdial  |  April 3, 2010 at 3:37 pm

      Whose home? It’s closer to the computer’s “home,” but further from what the student thinks the computer is. For a novice, the computer = YouTube, WWW, Wikipedia, iPod, and Wii. If computer science is an “esoteric notation that doesn’t result in anything remotely like computing,” then it’s going to be ignored.

      Reply
      • 8. Raymond Lister  |  April 3, 2010 at 6:23 pm

        With regard to misconceptions, abstractions, etc, maybe the following paper will be of interest:

        Smith, J., diSessa, A., & Roschelle, J. (1994). Misconceptions Reconceived: A Constructivist Analysis of Knowledge in Transition. Journal of the Learning Sciences, 3(2), 115-163.

        Abstract:
        This article uses a critical evaluation of research on student misconceptions in science and mathematics to articulate a constructivist view of learning in which student conceptions play productive roles in the acquisition of expertise. We acknowledge and build on the empirical results of misconceptions research but question accompanying views of the character, origins, and growth of students’ conceptions. Students have often been viewed as holding flawed ideas that are strongly held, that interfere with learning, and that instruction must confront and replace. We argue that this view overemphasizes the discontinuity between students and expert scientists and mathematicians, making the acquisition of expertise difficult to conceptualize. It also conflicts with the basic premise of constructivism: that students build more advanced knowledge from prior understandings. Using case analyses, we dispute some commonly cited dimensions of discontinuity and identify important continuities that were previously ignored or underemphasized. We highlight elements of knowledge that serve both novices and experts, albeit in different contexts and under different conditions. We provide an initial sketch of a constructivist theory of learning that interprets students’ prior conceptions as resources for cognitive growth within a complex systems view of knowledge. This theoretical perspective aims to characterize the interrelationships among diverse knowledge elements rather than identify particular flawed conceptions; it emphasizes knowledge refinement and reorganization, rather than replacement, as primary metaphors for learning; and it provides a framework for understanding misconceptions as both flawed and productive.

        Reply
  • […] in VPython for some of the labs and homework.  Danny used the oft-admired, gold-standard Force Concept Inventory.  The results weren’t great for the M&I […]

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  • […] Mark Guzdial summarized from Caballero’s thesis.  Basically, students were tested on the Force Concept Inventory after taking either a traditional physics class or a class using Matter and Interactions, and the […]

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  • […] enjoy Richard Hake’s posts. He has done excellent empirical educational research, so he knows what he’s talking about.  His posts are filled with links to all kinds of great […]

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