The present study combined subgoal learning and self-explanation frameworks to improve problem solving performance. Subgoal learning has been used to promote retention and transfer in procedural domains, such as programming. The primary method for learning subgoals, however, has been through passive learning methods, and passive learning methods are typically less effective than constructive learning methods. To promote constructive methods of learning subgoals, a subgoal learning framework was used to guide self-explanation. Self-explanation is an effective method for engaging learners to make sense of new information based on prior knowledge and logical reasoning. Self-explanation is typically more effective when learners receive some guidance, especially if they are novices, because it helps them to focus their attention on relevant information. In the present study, only some of the constructive learning methods produced better problem solving performance than passive learning methods. Learners performed best when they learned constructively and either received hints about the subgoals of the procedure or received feedback on the self-explanations that they constructed, but not when they received both hints and feedback. When students received both types of guidance, they did not perform better than those who learned subgoals through passive learning methods. These findings suggest that constructive learning of subgoals can further improve the benefits of learning subgoals, but there is an optimal level of guidance for students engaging in constructive learning. Providing too much guidance can be as detrimental as providing too little. This nuance is important for educators who engage their students in constructive learning and self-explanation to recognize and promote the best results.

Expository instructions, worked examples, and subgoal labels have all been shown to positively impact student learning and performance in computer science education. This study examined whether learning and problem solving performance differed based on the sequence of the instructional materials (expository and worked examples) and the presence of subgoal labels within the instructional materials. Participants were 138 undergraduate college students, age 17-25, who watched two instructional videos on creating an application in the App Inventor programming language before completing several learning assessments. 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. These manipulations did not affect cognitive load, novel problem solving performance, explanations of solutions, or the amount of time spent on instructions and completing the assessments. These results suggest that the order instructional materials are presented have has little impact on problem solving, although some benefit can be gained from presenting the worked example before the expository instructions when subgoal labels are included. This suggests the order the instructions are presented to learners does not impact learning. Previous studies demonstrating an effect of subgoal labels used text instructions as opposed to the video instructions used in the present study. Future research should investigate how these manipulations differ for text instructions and video instructions.

In science, technology, engineering, and mathematics (STEM) education, problem solving tends to be highly procedural, and these procedures are typically taught with general instructional text and specific worked examples. Instructional text broadly defines procedures for problem solving, and worked examples demonstrate how to apply procedures to problems. Subgoal labels have been used to help students understand the structure of worked examples, and this feature has increased problem solving performance. The present study explored using subgoal labels in instructional text to further improve learners’ problem solving performance. A factorial design examined the efficacy of subgoal labeled instructional text and worked examples for programming education. The results of the present study suggest that subgoal labels in instructional text can help learners in a different way than subgoal labels in worked examples. Subgoal labels in text helped the learner articulate the general procedure better, and subgoal labels in the example helped the learner apply those procedures better. When solving novel problems, learners who received subgoal labels in both the text and example performed better than those who received subgoal labels in only the example. Learners who received subgoal labels in only the example performed better than those who received subgoal labels in only the text and those who did not receive subgoal labels at all. The present study indicates that subgoal labeled instructional text can improve novices’ problem solving performance in programming, but subgoal labels must appear in both the text and example.