Using Text Mining to Characterize Online Discussion Facilitation
Journal of Asynchronous Learning Networks Volume 15, Number 2, ISSN 1939-5256
Facilitating class discussions effectively is a critical yet challenging component of instruction, particularly in online environments where student and faculty interaction is limited. Our goals in this research were to identify facilitation strategies that encourage productive discussion, and to explore text mining techniques that can help discover meaningful patterns in the discussions more efficiently at scale. Based on a close reading of selected discussion threads from online undergraduate science classes, we observed a variety of facilitation strategies associated with discussion quality. These observations informed our selection of a larger dataset of discussion threads to analyze via text mining techniques. Using latent semantic analysis to produce topic models of the content of the discussions, we constructed visualizations of the topical and temporal development of those discussions among students and faculty. These visualizations revealed patterns that appeared to correspond with specific facilitation styles and with the extent to which discussions remained focused on particular topics. From a case study focusing on six of these discussions, we documented distinct patterns in the types of facilitation strategies employed and the character of the discussions that followed. In our conclusion, we discuss potential applications of these analytical techniques for helping students, faculty, and faculty developers become more aware of their participation and influence in online discussions, thereby improving their value as a learning environment. (Contains 4 tables and 19 figures.)
Ming, N. & Baumer, E. (2011). Using Text Mining to Characterize Online Discussion Facilitation. Journal of Asynchronous Learning Networks, 15(2), 71-109.
Cited ByView References & Citations Map
Understanding Usage Patterns of Video-driven Discussion Using Singular Value and Semi-discrete Decomposition
Manav Malhotra & Zhou Zhou, EdLab, Teachers College, Columbia University, United States
EdMedia + Innovate Learning 2012 (Jun 26, 2012) pp. 1527–1533
These links are based on references which have been extracted automatically and may have some errors. If you see a mistake, please contact email@example.com.