Micro-Analysis of collaborative processes that facilitate productive online discussions: Statistical discourse analyses in three studies

Ming Ming Chiu, Inge Molenaar, Gaowei Chen, Alyssa Friend Wise, Nobuko Fujita

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Studying time with statistics can help shed light on cause-effect relationships in large online data sets and address three sets of research questions regarding sequences, time periods, and influences of phenomena across different time-scales. As such studies face many analytic difficulties (related to the data, dependent variables, or explanatory variables), this chapter shows how the method of Statistical Discourse Analysis (SDA) addresses each of them. Then, the authors apply SDA to three online data sets: (a) 183 participants' 894 messages in a mathematics forum without teacher moderation, (b) 17 students' 1,330 messages in a 13-week graduate course, and (c) 21 students' 252 messages across 8 weeks during a hybrid university course. Findings include (a) significant relationships between non-adjacent messages, (b) explanatory models of statistically-identified pivotal messages that distinguish distinct time periods, and (c) effects of larger phenomena on smaller phenomena (e.g., gender on message characteristics) and vice-versa (extensive summary on time periods).

Original languageEnglish (US)
Title of host publicationAssessment and Evaluation of Time Factors in Online Teaching and Learning
PublisherIGI Global
Pages232-263
Number of pages32
ISBN (Print)9781466646513
DOIs
StatePublished - 2013

ASJC Scopus subject areas

  • Social Sciences(all)

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    Chiu, M. M., Molenaar, I., Chen, G., Wise, A. F., & Fujita, N. (2013). Micro-Analysis of collaborative processes that facilitate productive online discussions: Statistical discourse analyses in three studies. In Assessment and Evaluation of Time Factors in Online Teaching and Learning (pp. 232-263). IGI Global. https://doi.org/10.4018/978-1-4666-4651-3.ch009