Multi‐Layered Temporal Network Analysis: Idea Improvement Processes in Knowledge‐Building Practice Leading to High Learning Performance

ABSTRACT Background This study aimed to develop and test new analytics for knowledge‐building practices from the transactive perspective. Based on a literature review, network analysis was identified as a promising analytical tool for these practices. We observed two aspects of network analysis that could be further developed: the multilayers of networks and temporality. Objectives Consequently, the study investigated transactive processes of collaborative learning leading to different learning performance levels using the multi‐layered temporal network analysis after examining the advantages of the multi‐layered temporal network analysis by comparing its findings with those of the traditional discourse network analysis. Methods This method was applied to identify multi‐layered temporal discourse patterns in knowledge‐building practices among first‐year university students engaged in project‐based learning. Discourse in each group was decomposed into discourse topics using exploratory clustering analysis with temporal changes in all nouns' degree centralities. Then, the multi‐layer discourse patterns were compared between groups with different learning performance levels. Results and Conclusions We identified two conditions for high learning performance not found by the traditional network discourse analysis: extensive comparison of multiple ideas and co‐elaboration through warranting. For idea improvement in knowledge‐building practices, the judgement of idea promisingness is crucial. Groups with high learning performance engaged in this judgement by contrasting multiple ideas, a strategy not found in groups with low learning performance. Further, of the two dimensions of idea improvement, the co‐elaboration process was evident in learners' discourse around their promising ideas, facilitated by warranting. Thus, the multi‐layered temporal network analysis of discourse could provide more detailed descriptions of how learners engage in their idea improvement processes. Comparative case studies suggest hypothetical conditions for successful learning processes.

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Publication Details

Journal
Journal of Computer Assisted Learning
Published
2025-05-26
DOI
https://doi.org/10.1111/jcal.70063
Citations
1
Primary Topic
Online and Blended Learning
Type
article
Field-Weighted Citation Impact
8.93

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article

Multi‐Layered Temporal Network Analysis: Idea Improvement Processes in Knowledge‐Building Practice Leading to High Learning Performance

Ritsuko Oshima, Jun Oshima, Anthony J. Taiki Kawakubo
1 citations
Journal of Computer Assisted Learning
Online and Blended Learning
8.93
article

Multi‐Layered Temporal Network Analysis: Idea Improvement Processes in Knowledge‐Building Practice Leading to High Learning Performance

Ritsuko Oshima, Jun Oshima, Anthony J. Taiki Kawakubo
article en
1 citations

Abstract

ABSTRACT Background This study aimed to develop and test new analytics for knowledge‐building practices from the transactive perspective. Based on a literature review, network analysis was identified as a promising analytical tool for these practices. We observed two aspects of network analysis that could be further developed: the multilayers of networks and temporality. Objectives Consequently, the study investigated transactive processes of collaborative learning leading to different learning performance levels using the multi‐layered temporal network analysis after examining the advantages of the multi‐layered temporal network analysis by comparing its findings with those of the traditional discourse network analysis. Methods This method was applied to identify multi‐layered temporal discourse patterns in knowledge‐building practices among first‐year university students engaged in project‐based learning. Discourse in each group was decomposed into discourse topics using exploratory clustering analysis with temporal changes in all nouns' degree centralities. Then, the multi‐layer discourse patterns were compared between groups with different learning performance levels. Results and Conclusions We identified two conditions for high learning performance not found by the traditional network discourse analysis: extensive comparison of multiple ideas and co‐elaboration through warranting. For idea improvement in knowledge‐building practices, the judgement of idea promisingness is crucial. Groups with high learning performance engaged in this judgement by contrasting multiple ideas, a strategy not found in groups with low learning performance. Further, of the two dimensions of idea improvement, the co‐elaboration process was evident in learners' discourse around their promising ideas, facilitated by warranting. Thus, the multi‐layered temporal network analysis of discourse could provide more detailed descriptions of how learners engage in their idea improvement processes. Comparative case studies suggest hypothetical conditions for successful learning processes.

Journal of Computer Assisted LearningVol. 41(3)
Shizuoka University (JP)
Ministry of Education, Culture, Sports, Science and Technology
Openalex Percentile: Top 4%
Online and Blended Learning
8.93
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