My colleagues Lauren Margulieux and Richard Catrambone (with Laura M. Schaeffer) have a new journal article out that I find fascinating. Lauren, you might recall, was a student of Richard’s who applied subgoal labeling to programming (see the post about her original ICER paper) and worked with Briana Morrison on several experiments that applied subgoal labeling to textual programming and Parson’s problems (see posts on Lauren’s defense and Briana’s).
In this new paper (see link here), they contrast subgoal labels across three different domains: Chemistry, statistics, and computer science (explicitly, programming). I’ve been writing lately about how learning programming differs from learning other STEM disciplines (see this post here, for example). So, I was intrigued to see this paper.
The paper contrasts subgoal labeled expository text (e.g., saying explicitly as a heading Compute Average Frequency) and subgoal labeled worked examples (e.g., saying Compute Average Frequency then showing the equation and the values and the computed result). I’ll jump to the punchline with the table that summarizes the result:
Programming has high complexity. Students learned best when they had both subgoal labeled text and subgoal labeled worked examples. Either one alone didn’t cut it. In Statistics, subgoal labeled examples are pretty important, but the subgoal labeled text doesn’t help much. In Chemistry, both the text and the worked examples improve performance, and there’s a benefit to having both. That’s an argument that Chemistry is more complex than Statistics, but less complex than Programming.
The result is fascinating, for two reasons. First, it gives us a way to empirically order the complexity of learning in these disciplines. Second, it gives us more reason for using subgoal labels in programming instruction — students just won’t learn as well without it.
