Statistical Discourse Analysis of an Online Discussion: Cognition and Social Metacognition
This study revised a statistical method (statistical discourse analysis or SDA) designed for linear sequences of turns of talk to apply to branches of messages in asynchronous online discussions. The revised SDA was used to test for cognitive and social m
- PDF / 182,439 Bytes
- 17 Pages / 439.37 x 666.142 pts Page_size
- 17 Downloads / 201 Views
Statistical Discourse Analysis of an Online Discussion: Cognition and Social Metacognition Ming Ming Chiu
Introduction This study considers how to revise a statistical method designed for face-to-face talk, statistical discourse analysis (SDA), to apply it to participant-coded online discussions (Fujita, Chap. 20, this volume). Unlike the linear sequence of turns of talk however, asynchronous online messages often branch out into separate threads. Applying a successful, revised SDA to online discussion can capitalize on participants’ self-coding of messages to enable analyses of large databases and extend online discussion research beyond messages’ aggregate attributes (e.g., Gress, Fior, Hadwin, & Winne, 2010) to relationships among messages. As earlier turns of talk affect later turns of talk, earlier online messages might influence later messages (Chiu, 2000a; Chiu, 2001; Jeong, 2006). Specifically, I examine how cognitive and social metacognitive aspects of earlier messages affect ideas and explanations in later messages. Whereas individual metacognition is monitoring and control of one’s own knowledge, emotions, and actions (Hacker & Bol, 2004), social metacognition is defined as group members’ monitoring and control of one another’s knowledge, emotions, and actions (Chiu & Kuo, 2009). By understanding how cognitive and social metacognitive components of recent online messages create a micro-time context that aid or hinder students’ ideas and explanations, educators can help students engage in beneficial online processes to learn more. This study contributes to the research literature in two ways. First, I introduce a new method to model branches of online messages across multiple topics. Second, this method tests how explanatory variables at multiple levels (individual characteristics, cognitive and social metacognitive aspects of messages) influenced 1,330
I appreciate the generous sharing of data by Nobuku Fujita, the research assistance of Choi Yik Ting, and comments from participants in the Alpine Rendez-Vous workshop. M.M. Chiu (*) University at Buffalo—State University of New York, Buffalo, NY, USA
417 D.D. Suthers et al. (eds.), Productive Multivocality in the Analysis of Group Interactions, Computer-Supported Collaborative Learning Series 16, DOI 10.1007/978-1-4614-8960-3_23, © Springer Science+Business Media New York 2013
418
M.M. Chiu
asynchronous online messages during a 13-week educational technology course. By examining students’ asynchronous online messages, researchers can build a more comprehensive understanding of students’ online processes and their influences to develop appropriate teacher interventions and computer environments.
Theoretical Framework Unlike students talking face-to-face, those in asynchronous online discussions can participate at different places and times, a valuable resource for improving their learning (Dubrovsky, Kiesler, & Sethna, 1991; Harasim, 1993; Tallent-Runnels et al., 2006). As students writing asynchronous, online messages have more time than those i
Data Loading...
