Cognitive Effect of Tracing Gesture in the Learning from Mathematics Worked Examples.

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Title: Cognitive Effect of Tracing Gesture in the Learning from Mathematics Worked Examples.
Authors: Yeo, Lian-Ming1 (AUTHOR), Tzeng, Yuh-Tsuen2 (AUTHOR) ttcytt@ccu.edu.tw
Source: International Journal of Science & Mathematics Education. Apr2020, Vol. 18 Issue 4, p733-751. 19p.
Subject Terms: *Cognitive learning, *Learning, *Level of difficulty, Gesture, Mathematics
Abstract: The present study attempted to replicate the previous results of Hu et al. (Learning and Instruction 35:85–93 2015) and further examined the boundary condition of tracing gesture whether its cognitive effect is merely comparable with other attention-guiding means, i.e., textual attention cueing, in two different learning tasks in nature. In two experiments, 11- to 13-year-old students were asked to study the worked examples on angles relationships involving parallel lines (experiment 1) and laws of exponents (experiment 2) either without tracing instruction, with textual attention cueing instruction, or tracing instruction. The tracing group outperformed the other two groups on a subsequent test and reported lower levels of test difficulty in experiment 1 but not in experiment 2, suggesting that the cognitive effect of tracing gesture was beyond guiding attention in the learning from highly visuospatial-based worked examples but not in the materials that are less obviously visuospatial in nature. The present findings suggested that tracing gesture offers a simple yet effective embodied technique that may further enhance the worked example-based learning through its cognitive effect specifically on the task involved with spatial information. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Science & Mathematics Education is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Cognitive Effect of Tracing Gesture in the Learning from Mathematics Worked Examples.
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Science+%26+Mathematics+Education%22">International Journal of Science & Mathematics Education</searchLink>. Apr2020, Vol. 18 Issue 4, p733-751. 19p.
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  Data: The present study attempted to replicate the previous results of Hu et al. (Learning and Instruction 35:85–93 2015) and further examined the boundary condition of tracing gesture whether its cognitive effect is merely comparable with other attention-guiding means, i.e., textual attention cueing, in two different learning tasks in nature. In two experiments, 11- to 13-year-old students were asked to study the worked examples on angles relationships involving parallel lines (experiment 1) and laws of exponents (experiment 2) either without tracing instruction, with textual attention cueing instruction, or tracing instruction. The tracing group outperformed the other two groups on a subsequent test and reported lower levels of test difficulty in experiment 1 but not in experiment 2, suggesting that the cognitive effect of tracing gesture was beyond guiding attention in the learning from highly visuospatial-based worked examples but not in the materials that are less obviously visuospatial in nature. The present findings suggested that tracing gesture offers a simple yet effective embodied technique that may further enhance the worked example-based learning through its cognitive effect specifically on the task involved with spatial information. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal of Science & Mathematics Education is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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