Statistical discourse analysis of online discussions: Informal cognition, social metacognition and knowledge creation

Ming Ming CHIU, Nobuko FUJITA

Research output: Chapter in Book/Report/Conference proceedingChapter

7 Citations (Scopus)

Abstract

To statistically model large data sets of knowledge processes during asynchronous, online forums, we must address analytic difficulties involving the whole data set (missing data, nested data and the tree structure of online messages), dependent variables (multiple, infrequent, discrete outcomes and similar adjacent messages), and explanatory variables (sequences, indirect effects, false positives, and robustness). Statistical discourse analysis (SDA) addresses all of these issues, as shown in an analysis of 1,330 asynchronous messages written and selfcoded by 17 students during a 13-week online educational technology course. The results showed how attributes at multiple levels (individual and message) affected knowledge creation processes. Men were more likely than women to theorize. Asynchronous messages created a micro-sequence context; opinions and asking about purpose preceded new information; anecdotes, opinions, different opinions, elaborating ideas, and asking about purpose or information preceded theorizing. These results show how informal thinking precedes formal thinking and how social metacognition affects knowledge creation. Copyright © 2014 by the Association for Computing Machinery, Inc.

Original languageEnglish
Title of host publicationLAK 2014: 4th International Conference on Learning Analytics and Knowledge
Place of PublicationNew York
PublisherAssociation for Computing Machinery
Pages217-225
ISBN (Print)1595930361, 9781595930361
DOIs
Publication statusPublished - 2014

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social cognition
discourse analysis
educational technology
student

Citation

Chiu, M. M., & Fujita, N. (2014). Statistical discourse analysis of online discussions: Informal cognition, social metacognition and knowledge creation. In LAK 2014: 4th International Conference on Learning Analytics and Knowledge (pp. 217-225). New York: Association for Computing Machinery. doi: 10.1145/2567574.2567580

Keywords

  • Statistical discourse analysis
  • Informal cognition
  • Social metacognition
  • Knowledge creation