The Learning Process for Education Research

One of the more influential projects in physics education (and learning sciences over all) was the effort by Jill Larkin and Herb Simon to characterize how students and experts solved physics problems. They found that students tend to look at physics problems at a shallow level, while experts see deep structure.  Students tend to look at what variables are present in the problem, match them to the equations given in class, and see what they can compute with those variables.  Experts look at a problem and identify the kind of problem it was, then work out a process to a solution.

My son is currently taking AP Physics, and I’m seeing this same process when he asks me for help.  My dissertation work was about teaching students kinematics by having them build simulations, so I’m familiar with some of the content.  I’m no expert, but am a bit closer than my son.  Matt brought me a problem then started with, “I can figure out delta-Y here, but can’t see why that’s useful.”  He knew the equation that matched the variables.  I drew a picture, then figured out what we needed to compute.  I then remembered the wrong equation (evidence that I’m no expert) and came up with an answer that clearly couldn’t be right. (Kudos to Matt for realizing that!)  Matt then figured out the right equation, and we came up with a more reasonable answer. I worked from the problem situation to an equation, and Matt started by looking for an equation.

I’ve been seeing this same process lately in how people come to understand education research.  I’m teaching an undergraduate and graduate (joint) class on educational technology this semester.  (We just started class last week.)  In the first week, I had them read two chapters of Seymour Papert’s Mindstorms; the paper “Pianos, not Stereos” by Mitchel Resnick, Amy Bruckman, and Fred Martin; and Jeanette Wing’s “Computational Thinking.”  I started the class discussion by asking for summary descriptions of the papers.  A Ph.D. student described Jeanette’s position as “Programming is useful for everyone to understand, because it provides useful tools and metaphors for understanding the world.”  I corrected that, to explain that Jeanette questions whether “programming” is necessary for gaining “computational thinking.”  The student shrugged off my comment with a (paraphrased) “Whatever.”  For those of us who care about computing education, that’s not a “whatever” issue at all — it’s a deep and interesting question whether someone can understand computing with little (or no?) knowledge of programming.  At the same time, the student can be excused for not seeing the distinction. It”s the first week of class, and it’s hard to see deep structure yet.  The surface level is still being managed. It’s hard to distinguish “learning programming” and “learning to think computationally,” especially for people who have learned to program.  “How else would you come to think computationally?”

This last week, we’ve been reviewing the findings from our first year of our Disciplinary Commons for Computing Educators where we had university and high school computer science teachers do Action Research in their own classrooms. Well, we tried to do Action Research.  We found that the teachers had a hard time inventing researchable questions about their own classrooms.  We ended up scaffolding the process, by starting out with experimental materials from others’ studies, so that teachers could simply pick the experiment that he or she felt would be most useful to replicate in his or her classroom.  We then found that the teachers did not immediately see how the results had any implication for their own classrooms.  It took us awhile to get teachers to even ask the questions: “The results show X (e.g., most students in my classroom never read the book).  What does that mean for my students?  Does that mean X is true for all my students?  Should I be doing something different in response?”

These results aren’t really surprising, either — at least in hindsight.  High school and university teachers have their jobs not because they are expert at education research.  University researchers typically are expert at some computing-related research, not computing education research, and a general “research perspective” doesn’t seem to transfer.  Our teachers were looking at the surface level, and it does take some particular knowledge about how to develop researchable questions and how to interpret results into an action plan afterwards.

Education research is a field of study.  I’ve been doing this kind of work for over 20 years, so you’d think I’d have realized that by now, but I still get surprised.  Simply being a teacher doesn’t make you an expert in education research, and being a domain researcher doesn’t make you an expert in education research in that domain.  It takes time and effort to see the deeper issues in education research, and everyone starts out just managing the surface features.