| Autor: |
Tomohiro Nagashima, Anna N. Bartel, Stephanie Tseng, Nicholas Allan Vest, Elena M Silla, Martha W. Alibali, Vincent Aleven |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Proceedings of the Annual Meeting of the Cognitive Science Society, vol 43, iss 43 |
| DOI: |
10.31219/osf.io/sbwfj |
| Popis: |
Although visual representations are generally beneficial for learners, past research also suggests that often only a subset of learners benefits from visual representations. In this work, we designed and evaluated anticipatory diagrammatic self- explanation, a novel form of instructional scaffolding in which visual representations are used to guide learners’ inference generation as they solve algebra problems in an Intelligent Tutoring System. We conducted a classroom experiment with 84 students in grades 5-8 in the US to investigate the effectiveness of anticipatory diagrammatic self-explanation on algebra performance and learning. The results show that anticipatory diagrammatic self-explanation benefits learners on problem-solving performance and the acquisition of formal problem-solving strategies. These effects mostly did not depend on students’ prior knowledge. We analyze and discuss how performance with the visual representation may have influenced the enhanced problem-solving performance. Published version available: https://escholarship.org/uc/item/17q6g4db |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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