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.
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.
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.
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.