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




Temporal analytics, Cluster analysis, Machine learning, Promising ideas, Idea analysis, Knowledge building discourse


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

Author Biographies

Alwyn Vwen Yen Lee, Nanyang Technological University

Alwyn Vwen Yen Lee is a Ph.D. student and research associate at the Nanyang Technological University, Singapore. He has been researching on learning analytics related to the understanding of learning, analysis of collective responsibility within students, and using pedagogical and technological methods to sustain creative work for improving ideas through Knowledge Building theory. As part of his Ph.D. studies, he contributes to research on data mining and discourse analytics within online discussions and has also published novel work in international academic conferences related to identification and analysis of ideas in online discourse. His other works as an electrical engineer include ambient sensor networks and rule-based approaches for activity recognition. At present, he is integrating technology in the field of computer sciences with educational theory to understand further students’ intentions for learning, and the applicability of skills outside of classrooms.

Seng Chee Tan, Nanyang Technological University

Seng Chee Tan is an associate professor and the acting co-director of the Centre for Research and Development in Learning (CRADLE@NTU) at the Nanyang Technological University, Singapore. He earned his Ph.D. in Instructional Systems from the Pennsylvania State University and joined Nanyang Technological University in 2000. Prior to the appointment in CRADLE, he has taken on different roles related to advancing the use of ICT in education, including the Head of Learning Sciences and Technologies academic group in the National Institute of Education, Singapore, and an Assistant Director in the Educational Technology Division in the Ministry of Education. He has taught courses on instructional design and learning sciences at the graduate level and has conducted professional courses for organizations such as the Ministry of Education.


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How to Cite

Lee, A. V. Y., & Tan, S. C. (2017). Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis. Journal of Learning Analytics, 4(3), 76–101.



Special Section: It's About Time: Temporal Analysis of Learning Data Part 1