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Title: Scaffolded self-explanation with visual representations promotes efficient learning in early algebra.
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.
Authors:
; ; ; ; ; ;
Editors:
Fitch, T.; Lamm, C.; Leder, H.; Teßmar-Raible, K.
Award ID(s):
1760947
Publication Date:
NSF-PAR ID:
10328976
Journal Name:
Proceedings of the 43rd Annual Conference of the Cognitive Science Society
Sponsoring Org:
National Science Foundation
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