Motivation: To explore the efficacy different types of guidance during a constructive learning activity (self-explanation of the subgoals of a programming problem-solving procedure) in an online learning environment.
Self-explanation of subgoals: Self-explanation is exactly what it sounds like. It is the process of a learner explaining to themselves (i.e., using their prior knowledge, new information, and logic) something that they are learning. It has been consistently effective for learning, except in fields that do not follow logical rules (e.g., learning how to pronounce English words; Wylie & Chi, 2014). In this study, we explored whether learners could successfully self-explain the subgoals of a programming procedure. The fundamentals of subgoal learning are explained in this post about our 2016 Computer Science Education paper.
This was an interesting question, building upon Morrison et al.’s (2015) work, because subgoal-oriented instructional materials are effective because learners tend to not recognize the underlying structure of problem solving processes by themselves. I have addressed this issue by explicitly telling learners the structure of the problem solving process through subgoal labels. However, from the learners’ perspective, this is a passive way of learning the subgoals of a procedure, and passive learning can be less effective than constructive learning. In this study, we explored whether learners could construct their own subgoal labels through self-explanation if they were given hints about the underlying structure.
Results: In the experiment, we had 10 different conditions that varied the type of guidance that learners received about the subgoals of the procedure. The conditions ranged from direct instruction (i.e., participants given subgoal labels) to completely unguided construction (i.e., participants told to create subgoal labels after a training activity, but no hints or feedback were given). We found that 2 conditions performed better than the other 8 conditions (no statistical differences among the remaining 8 conditions). Participant in both of the top performing conditions constructed their own subgoal labels with one of two types of guidance: hints, which gave clues about structural similarities in the problem before learners constructed their own labels , or feedback, which provided expert-created subgoal labels after learners constructed their own labels so that they could compare their labels to an expert’s. When participants received both the hints and the feedback, however, they did not perform as well as when they received only one or the other. In a surprising result, the quality of participant-created subgoal labels did not predict their performance on novel problems.
Why this is important: In constructivism, there’s a constant balancing act between providing enough guidance to increase learning efficacy and providing too much guidance that hinders knowledge construction. In some cases, direct instruction can be the most practical solution, and in other cases, unguided learning activities can foster the best outcomes. This study explores a new method for guiding self-explanation of problem solving procedures and tested variations on the direct-instruction-to-unguided-construction spectrum to pinpoint the Goldilocks-like effect of just-right guidance. Furthermore, it suggests that multiple types of guidance can interact in detrimental ways and that expert’s judgments of quality explanations might not align with actual learning outcomes.
Margulieux, L. E., & Catrambone, R. (2018, online). Finding the best types of guidance for constructing self-explanations of subgoals in programming. Journal of the Learning Sciences. Published online 6/26/18. doi: 10.1080/10508406.2018.1491852 Author Approved Manuscript
Morrison, B. B., Margulieux, L. E., & Guzdial, M. (2015). Subgoals, context, and worked examples in learning computing problem solving. In Proceedings of the 11th Annual International Conference on International Computing Education Research (pp. 21-29). ACM.
Wylie, R., & Chi, M. T. H. (2014). The self-explanation principle in multimedia learning. In R. Mayer (Ed.) The Cambridge Handbook of Multimedia Learning, 2nd Edition (pp.413-432). Cambridge University Press.
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