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Title: Analyzing Online Knowledge-Building Discourse Using Probabilistic Topic Models
Authors: Sun, Weiyi
Zhang, Jianwei
Jin, Hui
Lyu, Siwei
Issue Date: Jun-2014
Publisher: Boulder, CO: International Society of the Learning Sciences
Citation: Sun, W., Zhang, J., Jin, H., & Lyu, S. (2014). Analyzing Online Knowledge-Building Discourse Using Probabilistic Topic Models. In Joseph L. Polman, Eleni A. Kyza, D. Kevin O'Neill, Iris Tabak, William R. Penuel, A. Susan Jurow, Kevin O'Connor, Tiffany Lee, and Laura D'Amico (Eds.). Learning and Becoming in Practice: The International Conference of the Learning Sciences (ICLS) 2014. Volume 2. Colorado, CO: International Society of the Learning Sciences, pp. 823-830.
Abstract: This exploratory study tested the use of machine learning techniques, in particular, probabilistic topic models, to conduct automated analysis of the online discourse a Grade 4 knowledge-building community that investigated optics over three months using Knowledge Forum. Using the Latent Dirchilet Allocation (LDA) model, we extracted ten distinct and semantically meaningful clusters (i.e., topics) from the online discourse, which overlapped substantially with--although did not directly map onto--the inquiry themes identified by students and inquiry thread topics identified by researchers. The LDA analysis further identified discourse entries relevant to each of the topics, with acceptable agreement achieved between the automated analysis results and the manual coding of two researchers.
Appears in Collections:ICLS2014

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