Please use this identifier to cite or link to this item: https://repository.isls.org//handle/1/6402
Title: Enhancing Knowledge Building Discourse with Automated Feedback on Idea Complexity
Authors: Zhang, Jianwei
Yuan, Guangji
Zhong, Jiuning
Pellino, Sam
Chen, Mei-Hwa
Keywords: Design
Issue Date: Jun-2020
Publisher: International Society of the Learning Sciences (ISLS)
Citation: Zhang, J., Yuan, G., Zhong, J., Pellino, S., & Chen, M. (2020). Enhancing Knowledge Building Discourse with Automated Feedback on Idea Complexity. In Gresalfi, M. and Horn, I. S. (Eds.), The Interdisciplinarity of the Learning Sciences, 14th International Conference of the Learning Sciences (ICLS) 2020, Volume 3 (pp. 1697-1700). Nashville, Tennessee: International Society of the Learning Sciences.
Abstract: This study aims to improve student knowledge-building discourse with automated analysis and feedback. The automated analysis detects different levels of discourse contributions including questions, ideas, and information sources, achieving an acceptable level of consistency with human coding. The automated analysis was used to create an on-demand feedback tool embedded in Knowledge Forum/Idea Thread Mapper to inform student reflection on their online discourse. Research conducted in four grade 5 science classrooms tested the use of automated feedback for knowledge building. The preliminary results suggest that with the feedback, students were able to revise their notes and contribute more complex explanations as opposed to simple factual information.
URI: https://doi.dx.org/10.22318/icls2020.1697
https://repository.isls.org//handle/1/6402
Appears in Collections:ICLS 2020

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