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Title: Scaffolding Computational-Thinking Moves for Collective Knowledge Advancement in Multidisciplinary Collaboration
Authors: Feng, Xueqi
Chan, Carol K. K.
Zhao, Jianhua
Liu, Kun
Keywords: CSCL
Issue Date: 2022
Publisher: International Society of the Learning Sciences
Citation: Feng, X., Chan, C. K., Zhao, J., & Liu, K. (2022). Scaffolding computational-thinking moves for collective knowledge advancement in multidisciplinary collaboration. In Weinberger, A. Chen, W., Hernández-Leo, D., & Chen, B. (Eds.), Proceedings of the 15th International Conference on Computer-Supported Collaborative Learning - CSCL 2022 (pp. 487-490). International Society of the Learning Sciences.
Abstract: A knowledge-building environment mediated by Knowledge Forum® (KF) and enriched with students’ computational-thinking moves to advance collective knowledge was examined. Thirteen multidisciplinary higher education students studying Education and Modern Technology participated and worked in opportunistic groups. After engaging in collective inquiries, they constructed computational-thinking moves, which were put on KF for visualization and shared inquiry. These moves were adapted as scaffolds, followed by the formulation of Idea-Friend Maps based on learning analytics. Quantitative analyses show collective knowledge advancement and changing trajectories over time. Qualitative analyses reveal how students advanced collective knowledge with computational-thinking moves in multidisciplinary collaboration. This study offers insights into developing computational- thinking competencies through learning analytics and sheds light on the use of students’ explicit computational-thinking moves as scaffolds to advance collective knowledge.
Description: Short Paper
Appears in Collections:ISLS Annual Meeting 2022

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