Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis

Abstract

Understanding ideas in a discourse is challenging, especially in textual discourse analysis. We propose using temporal analytics with unsupervised machine learning techniques to investigate promising ideas for the collective advancement of communal knowledge in an online knowledge building discourse. A discourse unit network was constructed and temporal analysis was carried out to identify promising ideas, which are improvable perceptions of significant relevance that aid in the understanding of discourse context and content. With the aid of a degree centrality–betweenness centrality (DC-BC) graph, more promising ideas were discovered. An additional analysis using multiple DC-BC graph snapshots at different discourse junctures illustrates the transition of these promising ideas over time. Machine learning in the form of k-means clustering further categorized promising ideas. Cluster centroids were calculated and represented the foci of discussions, while the movement of discourse units about cluster centroids reflected how ideas affected learning behaviours among the participants. Discourse units containing promising ideas were qualitatively verified. Overall, the results showed that the implementation of temporal analytics and clustering provided insights and feedback to users about idea-related processes in the discourse. The findings have implications for teachers, students, and researchers