Optimal self-explanation prompt design in dynamic multi-representational learning environments

Self-explanation prompts are considered to be an important form of scaffolding in the comprehension of complex multimedia materials. However, there is little theoretical understanding to date of self-explaining prompt formats tailored to different expertise levels of learners to help them fully exploit the advantages of dynamic multi-representational materials. To address this issue, this study designed two types of self-explaining prompts: the reasoning-based prompts asked the learners to reason the action run of the animation; the predicting-based prompts asked the learners to predict the upcoming action of the animation, and then asked for reasoning if they made a wrong prediction. Furthermore, multiple indicators including learning outcome, cognitive load demand, learning time, and learning efficiency were used to interpret the prompts' effects on different expertise levels of learners. A total of 244 undergraduate students were randomly assigned to one of the three conditions: a control and two different self-explaining prompt conditions. The results indicate that the learning effects of self-explaining prompts depend on levels of learner expertise. Based on the results, this study makes recommendations for adaptive self-explaining prompt design.