Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse

Bodong Chen, Monica Resendes, Ching Sing Chai, Huang Yao Hong

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

As collaborative learning is actualized through evolving dialogues, temporality inevitably matters for the analysis of collaborative learning. This study attempts to uncover sequential patterns that distinguish “productive” threads of knowledge-building discourse. A database of Grade 1–6 knowledge-building discourse was first coded for the posts’ contribution types and discussion threads’ productivity. Two distinctive temporal analysis techniques–Lag-sequential Analysis (LsA) and Frequent Sequence Mining (FSM)–were subsequently applied to detecting sequential patterns among contribution types that distinguish productive threads. The findings of LsA indicated that threads that were characterized by mere opinion-giving did not achieve much progress, while threads having more transitions among questioning, obtaining information, working with information, and theorizing were more productive. FSM further uncovered from productive threads distinguishing frequent sequences involving sustained theorizing, integrated use of evidence, and problematization of proposed theories. Based on the significance of studying temporality in collaborative learning revealed in the study, we advocate for more analytics tapping into temporality of learning.

Original languageEnglish (US)
Pages (from-to)162-175
Number of pages14
JournalInteractive Learning Environments
Volume25
Issue number2
DOIs
StatePublished - Feb 17 2017

Keywords

  • Frequent Sequence Mining
  • Lag-sequential Analysis
  • Temporality
  • knowledge building
  • learning analytics

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