Detecting patterns of idea novelty and complexity in student knowledge building discourses

URI
Thumbnail Image
Type
Conference paper
Citation
Yuan, G., Teo, C. L., Chu, Z., Zhang, J., Chen, M.-H. F., Zhong, T., Chang-Sundin, C. Y., Lee, A. V. Y., & Ong, K. K. A. (2024). Detecting patterns of idea novelty and complexity in student knowledge building discourses. In J. Clarke-Midura, I. Kollar, X. Gu, & C. D’Angelo (Eds.), Proceedings of the 17th International Conference on Computer-Supported Collaborative Learning (pp. 241-244). International Society of the Learning Sciences. https://doi.org/10.22318/cscl2024.527505
Author
Yuan, Guangji 
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Teo, Chew Lee 
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Chu, Zheng
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Zhang, Jianwei
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Chen, Mei-Hwa F.
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Zhong, Tianlong
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Chang-Sundin, Chun Yen
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Lee, Alwyn Vwen Yen 
•
Ong, Aloysius Kian-Keong
Abstract
This paper explores the application of a framework for idea novelty in students’ discourse for knowledge building. Knowledge building promotes collaborative discourse among students and supports them in expanding collective community knowledge. However, students often go beyond the sharing of information, and they contribute novel ideas that are vital to deepening community knowledge and expanding collective inquiry. Novel ideas not only reveal the character and quality of the discourse but also show how the conversation may extend to deepen the understanding of a challenging topic. This study attempts to illuminate novel ideas from students as they engage in knowledge building using the analytical lens of novelty. Data analysis from exploratory analysis and multiple correspondence analysis revealed patterns of how students contribute novel ideas to sustain their conversation. Utilizing advanced Machine Learning techniques, this study effectively identified and quantified patterns of idea novelty and complexity in student discourses, enhancing the understanding of collaborative knowledge construction.
Date Issued
2024
ISBN
9798990698017 (online)
Publisher
International Society of the Learning Sciences
DOI
10.22318/cscl2024.527505
Description
The open access version is available at https://repository.isls.org//handle/1/10521