Learning involves the integration of new information into existing knowledge. Generoting explanations to oneself (self-explaining) facilitates that integration process. Previously, self-explanation has been shown to improve the acquisition of problem-solving skills when studying worked-out examples

representation, fourth graders were asked to indicate the location of colored flags by placing similarly colored stickers on a map. In the explaining condition, students wrote down what clues they had used; in the baseline condition, they placed stickers without explanation. The explaining students

Prompted self-explanation, tutored practice, worked examples, analogical comparisons, second

of satisfaction of its requirements. Since self-adaptive system requirements may themselves be emergent, a means needs to be found to explain the current behaviour of the system and the reasons that brought that behaviour about. We propose the use of goal-based models during runtime to offer self-explanation

in metareasoning to consider what it means for a system to understand itself in any meaningful way. The basic claim is that if an agent truly knows what it is doing and why, it should be able explain itself to others using natural language or some other interactive mechanism with humans. To perform self-explanation

Learning in multimedia environments is hard because it requires learners to actively comprehend and integrate information across diverse sources and modalities. Self-explanation is an effective learning strategy that helps learners develop deep understanding of complex phenomena and could be used

Testing the instructional fit hypothesis:

evidence of within plant productivity improvements that can be attributed to a liberalized trade for the plants in the import-competing sector. In many cases, aggregate productivity improvements stem from the reshuffling of resources and output from less to more efficient producers.

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Abstract: Self-explanation is an effective teaching/learning strategy that has been used in several intelligent tutoring systems in the domains of Mathematics and Physics to facilitate deep learning. Since all these domains are well structured, the instructional material to self-explain can