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Title: Supporting Students' Collective Ideas Improvement Through Learning Analytics-Augmented Meta-Discourse
Authors: Yu, Yawen
Chan, Carol K. K.
Teo, Chew Lee
Chen, Gaowei
Keywords: CSCL
Issue Date: 2022
Publisher: International Society of the Learning Sciences
Citation: Yu, Y., Chan, C. K., Teo, C. L., & Chen, G. (2022). Supporting students' collective ideas improvement through learning analytics-augmented meta-discourse. In Weinberger, A. Chen, W., Hern├índez-Leo, D., & Chen, B. (Eds.), Proceedings of the 15th International Conference on Computer-Supported Collaborative Learning - CSCL 2022 (pp. 274-281). International Society of the Learning Sciences.
Abstract: This study investigates how secondary school students engaged in collective inquiry enriched with analytics-supported meta-discourse in a knowledge-building environment. Twenty grade 11 students in a Singapore science classroom participated; the domain of the study was photosynthesis. Students engaged in knowledge-building inquiry mediated by Knowledge Forum (KF) and enriched with learning analytics-augmented meta-discourse over six weeks. Specifically, they employed the embedded KF analytics tools, word cloud to reflect on and continuously improve their collective ideas. Findings indicated that students improved their understanding of key concepts in photosynthesis and engaged in deepening discourse moves over time. Analysis of classroom dynamics, KF reflection and artefacts revealed that with the support of KF analytics-pedagogy, students engaged in meta-discourse, rising above, deepening, and identifying emerging research areas for collective knowledge advances. Implications for how to use analytics-supported to support meta-discourse for knowledge building are discussed.
Description: Long Paper
Appears in Collections:ISLS Annual Meeting 2022

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