Promoting learning, memory, and transfer in a time-constrained, high hazard environment

Two methods of metacognitive reflection for promoting compliance with an aviation safety rule were tested in a transfer design. Two groups of pilots (n = 10) conducted a simulated flight entailing a search for a target on the ground. During this flight, only 35% of the pilots stayed above an altitude of 500 ft, the minimum allowed by relevant regulations. Following the flight, one group completed a self-explanation questionnaire, in which they explained their actions during the initial flight and what they would do in future flights. The other group completed a relapse-prevention questionnaire, in which they identified the circumstances leading to safety lapses and their future avoidance. A third group (n = 10) formed a rest control; they conducted a familiarization flight without a ground target or debriefing. One week later, all pilots conducted a series of test flights with the same or different ground targets as the initial flight. The self-explanation group showed 100% compliance when the ground target remained the same, but less so (<70%) when the ground target was different. The relapse-prevention group and control groups both showed low levels of compliance across all test flights (<30%). The results are discussed from theoretical and applied perspectives.     

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.     

The role of co-explanation and self-explanation in learning from design examples of PowerPoint presentation slides

The current study investigated how people learn design principles from examples of PowerPoint presentation slides through self-explanation and co-explanation. This study also explored a strategy to improve the effectiveness of co-explanation by integrating it with a collaborative design activity. Preservice teachers (n = 120) studied the design examples of PowerPoint presentation slides in four research conditions: co-explanation with design, co-explanation, self-explanation, and no prompt (control). Pairs of learners in the co-explanation condition explained fewer strengths and weaknesses of the design examples than nominal pairs in the self-explanation condition. Moreover, co-explanation was not more effective than self-explanation when it came to individual learning outcomes. In contrast, pairs in the co-explanation with design condition were more actively engaged in co-explaining design examples than pairs in the co-explanation condition. This study shows that co-explanation with design is more beneficial for constructing and sharing knowledge of design principles than co-explanation only. This study discussed a trade-off between constructive/interactive learning effects and transactional activity costs in co-explaining design examples.     

Integrating self-explanation functionality into a complex game environment: Keeping gaming in motion

Previous research has shown that either asking students to explain their answers or providing explanatory feedback can be effective ways to increase learning from an educational game. This study focused on an educational physics game about Newton's 3 Laws of Motion called SURGE: The Fuzzy Chronicles. Eighty-six middle school students played one of three versions of the game: (1) the base version with no tips or questions, (2) the self-explanation version with self-explanation questions prompts, and (3) the explanatory feedback version with gameplay tips. There were no significant overall learning differences between the three groups, but students in the base version successfully answered more questions about Newton's second law than students in the self-explanation group. This may have been due to students in the base condition progressing significantly further through the game than students in the self-explanation group. The results suggest that the cognitive load for gameplay as well as game flow must be managed in order for students to take advantage of explanation functionality in educational tools designed to increase deeper, germane processing.     

Applying the self-explanation principle to multimedia learning in a computer-based game-like environment

What is the most effective way to incorporate self-explanation into an educational game? In Experiment 1, students who played a 10-level computer game about electrical circuits performed better on an embedded transfer test (i.e., level 10) if they were required to select the reason for each move from a list on levels 1–9 (selection self-explanation) than if they were not required to engage in self-explanation (d = 1.20). In Experiment 2, the same pattern of results was replicated (d = 0.71), but students who were required to type in their reason for each move on levels 1–9 (generation self-explanation) did not perform any better than those who were not required to engage in self-explanation (d = −0.06). Overall, asking students to select a reason from a list fosters some degree of reflection while not overly disrupting the flow of the game.