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Data · dataset · 2026

Huixin Gao: Audience Design as a Generative Mechanism in Proof Learning: Comparing Self-Explanation and Explanation to a Fictitious Other

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<p dir="ltr">Developing undergraduates’ ability to explain mathematical proofs is a core learning goal in mathematics education.

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Explanation tasks support this goal by encouraging students to articulate not just that a statement is true but why it is true, making explicit the chain of reasoning underpinning each inferential step. Existing research has largely focused on examining explanation strategies in isolation, leaving it unclear which approach best supports the development of high-quality explanations.

This study investigated how two explanation strategies—self-explanation (SE) and explanation to a fictitious low-prior-knowledge audience (EFO-weak)—affect proof-related explanatory performance, and whether the assumed prior knowledge of the audience (weak vs. high) modulates this performance. Undergraduate students submitted oral video explanations and anonymously assessed peers’ submissions using structured rubrics. Quantitative analyses revealed that EFO-weak encourages better explanations than SE, suggesting that framing explanations for an external audience promotes process-oriented reasoning and the careful articulation of warrants to a greater extent.

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No significant difference emerged between EFO-weak and EFO-high, though interviews indicated students adjusted explanations based on the recipient’s assumed knowledge. Theoretically, this study demonstrates the significance of audience design as a potentially generative mechanism in proof learning. It provides direct comparative evidence that the intended recipient—self versus a fictitious other—shapes the quality and structure of students’ explanations, consistent with the learning-by-teaching effects documented in educational psychology.

Simultaneously, the findings underscore the need for greater precision in how explanation quality and conceptual understanding are operationalised and linked, to better capture the cognitive processes underlying effective proof learning.</p>

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Video 75%
Provenance · 1 source records, 9 field assertions
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