The critical part of PCK: What students get wrong

May 13, 2013 at 1:01 am 5 comments

I’ve written before about computer science pedagogical content knowledge (PCK).  Phil Sadler and his colleagues just published a wonderful study about the value of PCK.  He found that science teachers need to know science, but the most effective science teachers also know what students get wrong — their misconceptions, what the learning difficulties are, and what are the symptoms of misunderstandings.  I got a chance to ask him about this paper, and he said one of the implications of the work that he sees is that he offers a way to measure PCK, and measuring something important about teaching is hard and useful.

For the study described in their paper, Sadler and his colleagues asked teachers to answer each question twice, once to give the scientifically correct answer, and the second time to predict which wrong answer their students were likeliest to choose. Students were then given the tests three times throughout the year to determine whether their knowledge improved.

The results showed that students’ scores showed the most improvement when teachers were able to predict their students’ wrong answers.

“Nobody has quite used test questions before in this way,” Sadler said. “What I had noticed, even before we did this study, was that the most amazing science teachers actually know what their students’ wrong ideas are. It occurred to us that there might be a way to measure this kind of teacher knowledge easily without needing to spend long periods of time observing teachers in their classrooms.”

via Understanding student weaknesses | Harvard Gazette.

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Heading to UMBC for Computing Education Summit Sovereignty, Open Source, and Sacred Knowledge: Learning about STEM Education for Indigenous Peoples

5 Comments Add your own

  • 1. Neil Brown  |  May 13, 2013 at 3:59 am

    I can’t help but feel that there’s a lot in this idea. It seems to fit very well with Mazur and Muller’s work on strong existing student misconceptions about science, and replacing them by explicitly calling them out. I begin to envisage a model teaching partly being about imparting knowledge, and also about debugging the student’s understanding/mental model — testing if they have got the right mental model, looking for common problems, and then fixing them.

    Of course, a lot of teachers do teach defensively, to try and iron these out ahead of time: e.g. explicitly calling out common mistakes ahead of time and warning to avoid them (things like pointing out false friends in language learning). For debugging, I’m reminded of Beth Simon et al’s recent Peer Instruction work where she was deliberately constructing examples where students were likely to show a shallow or incomplete model of programming, e.g. they would have problems with loops that start at 1, or go backwards, or have less than vs less than or equal to end conditions. As I understand it, Simon et al said there was a fine art in example construction, to make sure it drew out common student misconceptions so that they could be discussed and corrected.

    Reply
  • […] to Mark Guzdial for the original link to the paper. For another some comments by the authors on the study, see this […]

    Reply
  • 3. The Power of Misconception | Science Edventures  |  June 6, 2013 at 6:33 pm

    […] Neil pointed me to another summary of Sadler et al. by Mark Guzdial (@guzdial on Twitter) “The critical part of PCK: What students get wrong” with links to computer science […]

    Reply
  • […] thought was, “What a great idea!”  We need better ways to measure teaching (like with Sadler’s recent work), and this seems like a great first step.  I was surprised to read the […]

    Reply
  • […] thought was, “What a great idea!”  We need better ways to measure teaching (like with Sadler’s recent work), and this seems like a great first […]

    Reply

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