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Title: Using Anticipatory Diagrammatic Self-explanation to Support Learning and Performance in Early Algebra
Authors: Nagashima, Tomohiro
Bartel, Anna N.
Yadav, Gautam
Tseng, Stephanie
Vest, Nicholas A.
Silla, Elena M.
Alibali, Martha W.
Aleven, Vincent
Keywords: Learning Sciences
Issue Date: Jun-2021
Publisher: International Society of the Learning Sciences
Citation: Nagashima, T., Bartel, A. N., Yadav, G., Tseng, S., Vest, N. A., Silla, E. M., Alibali, M. W., & Aleven, V. (2021). Using Anticipatory Diagrammatic Self-explanation to Support Learning and Performance in Early Algebra. In de Vries, E., Hod, Y., & Ahn, J. (Eds.), Proceedings of the 15th International Conference of the Learning Sciences - ICLS 2021. (pp. 474-481). Bochum, Germany: International Society of the Learning Sciences.
Abstract: Prior research shows that self-explanation promotes understanding by helping learners connect new knowledge with prior knowledge. However, despite ample evidence supporting the effectiveness of self-explanation, an instructional design challenge emerges in how best to scaffold self-explanation. In particular, it is an open challenge to design self-explanation support that simultaneously facilitates performance and learning outcomes. Towards this goal, we designed anticipatory diagrammatic self-explanation, a novel form of self-explanation embedded in an Intelligent Tutoring System (ITS). In our ITS, anticipatory diagrammatic self-explanation scaffolds learners by providing visual representations to help learners predict an upcoming strategic step in algebra problem solving. A classroom experiment with 108 middle-school students found that anticipatory diagrammatic self-explanation helped students learn formal algebraic strategies and significantly improve their problem-solving performance. This study contributes to understanding of how self-explanation can be scaffolded to support learning and performance.
Appears in Collections:ISLS Annual Meeting 2021

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