Please use this identifier to cite or link to this item: https://repository.isls.org//handle/1/2481
Title: ACODEA: A Framework for the Development of Classification Schemes for Automatic Classifications of Online Discussions
Authors: Mu, Jin
Stegmann, Karsten
Mayfield, Elijah
Rose, Carolyn
Fischer, Frank
Issue Date: Jun-2011
Publisher: International Society of the Learning Sciences
Citation: Mu, J., Stegmann, K., Mayfield, E., Rose, C., & Fischer, F. (2011). ACODEA: A Framework for the Development of Classification Schemes for Automatic Classifications of Online Discussions. In Spada, H., Stahl, G., Miyake, N., & Law, N. (Eds.), Connecting Computer-Supported Collaborative Learning to Policy and Practice: CSCL2011 Conference Proceedings. Volume I — Long Papers (pp. 438-445). Hong Kong, China: International Society of the Learning Sciences.
Abstract: Research related to online discussions frequently faces the problem of analyzing huge corpora. Natural Language Processing technologies may allow automating the analysis. However, the state-of-the-art in machine learning and text mining approaches yields models that do not transfer well between corpora related to different topics. Also segmenting is a necessary step, but frequently, trained models are very sensitive to the particulars of the segmentation that was used when the model was trained. Therefore, in prior published research on text classification in a CSCL context, the data has been segmented by hand. We discuss work towards overcoming these challenges. We present a framework for developing coding schemes optimized for automatic segmentation and topic independent coding that builds on this segmentation. Our results show that our coding scheme can be fully automated by using a tool called SIDE. Finally, we discuss how fully automated analysis can enable context-sensitive support for collaborative learning.
URI: https://doi.dx.org/10.22318/cscl2011.438
https://repository.isls.org//handle/1/2481
Appears in Collections:CSCL 2011

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