Effects of Worked Examples with Explanation Types and Learner Motivation on Cognitive Load and Programming Problem-solving Performance

This study examined the effects of worked examples with different explanation types and novices’ motivation on cognitive load, and how this subsequently influenced their programming problem-solving performance. Given the study’s emphasis on both instructional approaches and learner motivation, the Cognitive Theory of Multimedia Learning served as the theoretical framework, as it integrates instructional design with motivational perspectives. The participants consisted of 75 university students who were non-computer majors and enrolled in their first programming course. A 2 × 2 between-subjects ANOVA design was employed, with two factors: explanation type (worked examples with instructional explanations vs. worked examples with guided questions to prompt self-explanations) and learner motivation level (high-motivated vs. less-motivated). The dependent variables included cognitive load components experienced by learners during learning and learning outcomes measured by retention and transfer performance. The results showed that (a) the worked example effect could reduce extraneous load and manage intrinsic load, thereby enhancing retention performance; (b) the combined effects of worked examples and guided self-explanations could benefit transfer learning, regardless of learners’ motivation; and (c) the role of motivation was evident, as high-motivated learners exhibited better retention and transfer performance by exerting more cognitive effort, regardless of instructional approach. The findings suggest that combining worked examples with guided questions to prompt self-explanations through in-code commenting is an effective and constructive activity, enabling novices to focus on actual learning without expending excessive cognitive effort.