Statistical Discourse Analysis: A method for modeling online discussion processes

Main Article Content

MIng Ming Chiu
Nobuko Fujita

Abstract

Online forums (synchronous and asynchronous) offer exciting data opportunities to analyze how people influence one another through their interactions. However, researchers must address several analytic difficulties involving the data (missing values, nested structure [messages within individuals within topics], non-sequential messages), outcome variables (discrete outcomes, rare instances, multiple outcome variables, similarities among nearby messages), and explanatory variables (sequences of explanatory variables, indirect mediation effects, false positives, and robustness of results). We explicate a method that addresses these difficulties (Statistical Discourse Analysis or SDA) and illustrate it on 1,330 asynchronous messages written and self-coded by 17 students during a 13-week online educational technology course. Both individual characteristics and message attributes were linked to participants’ online messages. Men wrote more messages about their theories than women did. Moreover, some sequences of messages were more likely to precede other messages. For example, opinions were often followed by elaborations, which were often followed by theorizing. 

Article Details

How to Cite
Chiu, M. M., & Fujita, N. (2014). Statistical Discourse Analysis: A method for modeling online discussion processes. Journal of Learning Analytics, 1(3), 61-83. https://doi.org/10.18608/jla.2014.13.5
Section
Special section: LAK'14 selected and invited papers
Author Biographies

MIng Ming Chiu, Purdue University

Charles Hicks Professor of Educational Psychology, Department of Educational Studies

Nobuko Fujita, University of Windsor

Assistant Professor,

Faculty of Education and Development