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

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 b...

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Detalles Bibliográficos
Publicado en:Journal of Computer Assisted Learning Vol. 41; no. 3; pp. 1 - 20
Autores principales: Oshima, Jun, Oshima, Ritsuko, Taiki Kawakubo, Anthony J.
Formato: pictorial research tables/charts Journal Article
Publicado: Wiley-Blackwell Jun2025
Acceso en línea:Ver este registro en EBSCOhost
Descripción
Sumario: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.