Please use this identifier to cite or link to this item: https://repository.isls.org//handle/1/6402
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dc.contributor.authorZhang, Jianwei
dc.contributor.authorYuan, Guangji
dc.contributor.authorZhong, Jiuning
dc.contributor.authorPellino, Sam
dc.contributor.authorChen, Mei-Hwa
dc.date.accessioned2020-07-08T23:53:48Z
dc.date.accessioned2020-07-09T04:29:27Z-
dc.date.available2020-07-08T23:53:48Z
dc.date.available2020-07-09T04:29:27Z-
dc.date.issued2020-06
dc.identifier.citationZhang, 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.en_US
dc.identifier.urihttps://doi.dx.org/10.22318/icls2020.1697
dc.identifier.urihttps://repository.isls.org//handle/1/6402-
dc.description.abstractThis 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.en_US
dc.language.isoenen_US
dc.publisherInternational Society of the Learning Sciences (ISLS)en_US
dc.subjectDesignen_US
dc.titleEnhancing Knowledge Building Discourse with Automated Feedback on Idea Complexityen_US
dc.typeShort Paperen_US
Appears in Collections:ICLS 2020

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