Archive for November 17, 2017

Parsons Problems have same Learning Gains as Writing or Fixing code, in less time: Koli Calling 2017 Preview

On Saturday, Barbara Ericson will be presenting at Koli Calling her paper (with Lauren Margulieux and Jeff Rick), “Solving Parsons Problems Versus Fixing and Writing Code.”

The basic design of her experiment is pretty simple.  Everybody gets a pretest where they answer multiple-choiced questions, write some code, fix some code, and solve some Parsons problems.  (I’ve written about Parsons Problems here before.)

Then there are three instructional treatments with three different kinds of problem-solving practice:

  • One group gets Parsons Problems with distractors in them — blocks that should not be dragged into the solution.
  • One group gets the same code to fix — same code as in the Parsons Problems but all the distractors are there.  They have to fix the broken code in the distractor to get to the same code as the correct block in the Parsons.
  • One group gets to write the code to solve the same problem.

Then they take an isomorphic (same basic problems with context and constants changed) post-test, go away, and come back one week later for a retention test (which is isomorphic to both the pretest and the first posttest: multiple choice questions, Parsons, fix code, write code).  So we have students who study with Parsons Problems getting tested by writing and fixing code.

Here’s the bottom line from their abstract: “We found that solving two-dimensional Parsons problems with distractors took significantly less time than fixing code with errors or than writing the equivalent code. Additionally, there was no statistically significant difference in the learning performance, or in student retention of the knowledge one week later.”

That’s it. It’s simple but profound.  Below is the timing table from the paper. The Parsons Problems took effort, but always less time — sometimes they took only half the time of fixing or writing code, and other times it was only a few percentage less. But it was always less.

One takeaway idea is: If Parsons leads to the same learning in less time, why wouldn’t every teacher use more Parsons problems?  A second one that we’ve been thinking alot about is: Can we provide more Parsons problems so that in the same amount of time that students were writing code, they actually learn more? Efficiency matters, as Elizabeth Patitsas’s work suggests — more efficient learning may mean less belief in Geek Gene by CS teachers.


November 17, 2017 at 7:00 am 11 comments

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