Building models explaining student participation behavior in asynchronous online discussion. (March 2016)

• Record Type: Journal Article
• Authors: Goggins, Sean; Xing, Wanli
• Is Part Of: Computers & education. Volume 94(2015)
• Page Start: 241
• Page End: 251
• Publication Date: 2016-03
• Subjects: Asynchronous online discussion -- Social cognitive theory -- Social ability -- Collective efficacy -- Partial least squares; Education -- Data processing -- Periodicals; Education -- Periodicals; Computers -- Periodicals; Computer-Assisted Instruction -- Periodicals; Éducation -- Informatique -- Périodiques; Electronic journals; 370.285
• DOI: 10.1016/j.compedu.2015.11.002
• Languages: English
• ISSNs: 0360-1315

Abstract

Abstract: Previous studies have invested much effort in understanding how participation in asynchronous online discussion affects student learning, and what factors influence student participation behavior. Results of these studies have been inconclusive and these investigations are often conducted from isolated perspectives. Relying on social cognitive theory, this study proposes two dynamic student participation models in online dialogue and particularly highlights understudied factors – collective efficacy, social ability, reading behavior, the time dimension of participation – to examine the mediation and causal relationship among those factors and their influence on learning. The models are tested utilizing data collected from a large US university. Specifically, while the predictive constructs are operationalized through the survey instruments, the outcome measures are modeled using electronic trace data and actual evaluation information. Data is analyzed using the Partial Least Squares modeling method. Results demonstrate the intertwined relationship among constructs and a different influencing mechanism for each construct on participation behavior and learning. By comparing these two built models, the time dimension of participation is shown to be more influential in predicting student learning than posting and reading actions. The paper concludes with a discussion of the implications of this study. Highlights: Shows two social cognitive theory informed online student participation models. Applies collective efficacy, social ability, and temporal dimensions to online learning. Computational model shows the relationship between participation behavior and learning. The time dimension of model predicts student learning.