Learning how to use a computer-based concept-mapping tool: Self-explaining examples helps

Abstract

In initial skill acquisition in well-structured domains, example-based learning typically leads to better learning outcomes than learning by doing. Cognitive Load Theory explains this result by the worked-example effect: Example-based learning prevents learners from using load-intensive strategies and focuses their attention on the principles to-be-learned. In two experiments, we investigated the use of examples for acquiring a new learning strategy, namely computer-based concept mapping. Experiment 1 compared learners who studied two examples on how to construct a concept map with learners who practiced concept mapping by constructing two concept maps on their own. We did not find significant differences in learning outcomes. Therefore, in Experiment 2, we introduced a third group of learners who studied examples with the additional support of self-explanation prompts. Self-explaining examples led to better learning outcomes than learning with examples without prompts or practicing. With respect to cognitive load, we found that examples without prompts released learners’ working memory compared to practicing, whereas self-explaining examples led to a higher cognitive load compared to examples without self-explanation.