This study discusses differences in problem solving performance among different domains presumably caused by the same instructional intervention. Discpline-based education research (DBER) acknowledges similarities in learners’ cognitive architecture that allow interventions to transfer among domains, but it also argues that each domain has characteristics that might affect how interventions impact learning. The present study uses an instructional design technique that had previously improved learners’ problem solving performance in programming: subgoal labeled procedural instructions and worked examples. The present study explores the effect of this technique for solving problems in statistics and chemistry. The problem solving procedures in the three domains have different characteristics. Similarly, each of the three experiments has a different pattern of results for problem solving performance. This study concludes that subgoal labeled worked examples seem to be equally effective across the different domains. Subgoal labeled procedural instructions, however, seem to be most effective for more complex procedures.

The present study explored passive, active, and constructive methods of learning problem solving procedures. Using subgoal learning, which has promoted retention and transfer in procedural domains, the study compared the efficacy of different methods for learning a programming procedure. The results suggest that constructive methods produced better problem solving performance than passive or active methods. The amount of instructional support that learners received in the three different constructive interventions also affected performance. Learners performed best when they either received hints about the subgoals of the procedure or received feedback on the subgoal labels that they constructed, but not when they received both. These findings suggest that in some cases constructing subgoal labels is better than passively or actively engaging with subgoal labels. There is an optimal level of instructional support for students engaging in constructive learning and that providing too much support can be equally as detrimental as providing too little support.

Subgoal labeled expository instructions and worked examples have been shown to positively impact student learning and performance in computer science education. This study examined whether problem solving performance differed based on the order of expository instructions and worked examples and the presence of subgoal labels within the instructions for creating applications (Apps) for phones. Participants were 132 undergraduates. A significant interaction showed that when learners were presented with the worked example followed by the expository instructions containing subgoal labels, the learner was better at outlining the procedure for creating an application. However, the manipulations did not affect novel problem solving performance or explanations of solutions,. These results suggest that some limited benefit can be gained from presenting a worked example before expository instructions when subgoal labels are included.