Please use this identifier to cite or link to this item: http://hdl.handle.net/10497/18864
Title: Understanding idea flow: Applying learning analytics in discourse
Authors: Lee, Alwyn Vwen Yen
Tan, Seng Chee, 1965-
Keywords: Learning analytics,
Discourse analysis
Knowledge building discourse
Idea flow
Education technology
Issue Date: 2017
Citation: Lee, A. V. Y., & Tan, S. C. (2017). Understanding idea flow: Applying learning analytics in discourse. Learning: Research and Practice, 3(1), 12-29. http://dx.doi.org/10.1080/23735082.2017.1283437
Abstract: The assessment and understanding of students’ ideas in discourse have often been a difficult problem for teachers to tackle. Recent innovations and technologies such as text mining can provide a partial solution by generating an estimated count of important keywords which are representative of ideas within discourse. However, investigating idea development and flow within discourse is a much more challenging task, and requires elaborate processing and analysis. In this study, a method for analysing idea flow was proposed and tested: (a) text mining and network analysis are employed to identify and validate ideas from textual discourse; (b) identified ideas are grouped together and mapped to relevant learning objectives; (c) groups of ideas are then aggregated using a binning method and scored; (d) a flow diagram is generated using the aggregated scores to visualize idea flow within discourse. By understanding how ideas flow within discourse, discussed key ideas can be monitored and any lapses in student understanding can be identified so that teachers will have information to provide timely interventions to support and scaffold learning.
Description: This is the final draft, after peer-review, of a manuscript published in Learning: Research and Practice. The published version is available online at http://www.tandfonline.com/10.1080/23735082.2017.1283437
URI: http://hdl.handle.net/10497/18864
ISSN: 2373-5082 (print)
2373-5090 (online)
Other Identifiers: 10.1080/23735082.2017.1283437
Appears in Collections:Journal Articles

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