Exploring the relationship between online discourse and commitment in Twitter professional learning communities. (November 2018)

• Record Type: Journal Article
• Authors: Xing, Wanli; Gao, Fei
• Is Part Of: Computers & education. Volume 126(2018)
• Page Start: 388
• Page End: 398
• Publication Date: 2018-11
• Subjects: Online communities -- Professional learning -- Data mining -- Survival analysis -- Online discourse; Education -- Data processing -- Periodicals; Education -- Periodicals; Computers -- Periodicals; Computer-Assisted Instruction -- Periodicals; Éducation -- Informatique -- Périodiques; Electronic journals; 370.285
• DOI: 10.1016/j.compedu.2018.08.010
• Languages: English
• ISSNs: 0360-1315

Abstract

Abstract: Educators show great interest in participating in social-media communities, such as Twitter, to support their professional development and learning. The majority of the research into Twitter-based professional learning communities has investigated why educators choose to use Twitter for professional development and learning and what they actually do in these communities. However, few studies have examined why certain community members remain committed and others gradually drop out. To fill this gap in the research, this study investigated how some key features of online discourse influenced the continued participation of the members of a Twitter-based professional learning community. More than 600, 000 tweets generated over six years under the hashtag #edchat were gathered. Online discourse was deconstructed to the cognitive dimension, the interactive dimension, and the social dimension. Text-mining methods were then used to automatically identify these dimensions in the tweets. Finally, survival analysis was used to quantify the influences of these dimensions on users' commitment time to the Twitter community. The implications of the results and findings are then discussed. Wanli Xing is an Assistant Professor in Instructional Technology at Texas Tech University, USA with background in learning sciences, statistics, computer science and mathematical modeling. His research interests are educational data mining, learning analytics, and CSCL. Gao Fei is an Associate Professor at Bowling Green State University. Her current research involves examining the types of interaction and learning enabled by online social technologies, designing technology-mediated environments that encourage meaningful social interaction, and exploring pedagogical methods that promote deep learning in such environments. Highlights: First large-scale longitudinal study for professional learning communities. Design data mining methodology to explore the large-scale text data. Quantifying the effect of online discourse on learners' commitment.