Understanding idea flow: applying learning analytics in discourse. (2nd January 2017)

• Record Type: Journal Article
• Authors: Lee, Alwyn Vwen Yen; Tan, Seng Chee
• Is Part Of: Learning. Volume 3:Number 1(2017)
• Page Start: 12
• Page End: 29
• Publication Date: 2017-01-02
• Subjects: Learning analytics -- discourse analysis -- knowledge building discourse -- idea flow -- education technology; Learning -- Research -- Periodicals; Learning -- Periodicals; Learning; Learning -- Research; Periodicals; 370.152305
• DOI: 10.1080/23735082.2017.1283437
• Languages: English
• ISSNs: 2373-5082

Abstract

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: (1) text mining and network analysis are employed to identify and validate ideas from textual discourse; (2) identified ideas are grouped together and mapped to relevant learning objectives; (3) groups of ideas are then aggregated using a binning method and scored; (4) a flow diagram is generated using the aggregated scores to visualise 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.