Please use this identifier to cite or link to this item: https://repository.isls.org//handle/1/9184
Title: Examining and Scaffolding Collective Idea Improvement in Knowledge Building Using Analytics and Meta-Discourse
Authors: Yu, Yawen
Chan, Carol K. K.
Teo, Chew Lee
Chen, Gaowei
Keywords: CSCL
Issue Date: 2023
Publisher: International Society of the Learning Sciences
Citation: Yu, Y., Chan, C. K., Teo, C. L., & Chen, G. (2023). Examining and scaffolding collective idea improvement in Knowledge Building using analytics and meta-discourse. In Damșa, C., Borge, M., Koh, E., & Worsley, M. (Eds.), Proceedings of the 16th International Conference on Computer-Supported Collaborative Learning - CSCL 2023 (pp. 163-170). International Society of the Learning Sciences.
Abstract: Knowledge Building (KB) engages students in meta-discourse to improve collective ideas. Multiple studies have examined the roles of learning analytical tools in supporting meta-discourse, while this study focused on the supportive roles of the KF-embedded tools. We designed a learning environment that engages meta-discourse among students with the assistance of word cloud and idea-building tools, both of which are embedded in KF. A total of 22 Singapore secondary school students participated in a six-weeks long study to learn photosynthesis in a KB learning environment. We adopted social network, inferential statistical, and semantic analyses. The results show that students’ learning gain is correlated with the number of posts in KF. In addition, the Learning Analytics-augmented meta-discourse supported students’ KF participation and collective idea improvement. Qualitative analysis showed how students engaged in meta-discourse sessions. The methodological and practical contributions were discussed.
Description: Long Paper
URI: https://doi.org/10.22318/cscl2023.276803
https://repository.isls.org//handle/1/9184
Appears in Collections:ISLS Annual Meeting 2023

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