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Please use this identifier to cite or link to this item: http://hdl.handle.net/1820/6483
Title: ReaderBench: Automated evaluation of collaboration based on cohesion and dialogism
Authors: Dascalu, Mihai
Trausan-Matu, Stefan
McNamara, Danielle
Dessus, Philippe
Keywords: Computer supported collaborative learning
Dialogism
Collaboration assessment
Automated feedback
Learning analytics
Cohesion-based discourse analysis
Issue Date: Oct-2015
Publisher: International Society of the Learning Sciences, Inc.
Citation: Dascalu, M., Trausan-Matu, S., McNamara, D.S., & Dessus, P. (2015). ReaderBench – Automated Evaluation of Collaboration based on Cohesion and Dialogism. International Journal of Computer-Supported Collaborative Learning, 10(4), 395–423. doi: 10.1007/s11412-015-9226-y
Series/Report no.: International Journal of Computer-Supported Collaborative Learning, 10(4), 395–423;
Abstract: As Computer-Supported Collaborative Learning (CSCL) gains a broader usage, the need for automated tools capable of supporting tutors in the time-consuming process of analyzing conversations becomes more pressing. Moreover, collaboration, which presumes the intertwining of ideas or points of view among participants, is a central element of dialogue performed in CSCL environments. Therefore, starting from dialogism and a cohesion-based model of discourse, we propose and validate two computational models for assessing collaboration. The first model is based on a cohesion graph and can be perceived as a longitudinal analysis of the ongoing conversation, thus accounting for collaboration from a social knowledge-building perspective. In the second approach, collaboration is regarded from a dialogical perspective as the intertwining or synergy of voices pertaining to different speakers, therefore enabling a transversal analysis of subsequent discussion slices.
Description: Dascalu, M., Trausan-Matu, S., McNamara, D.S., & Dessus, P. (2015). ReaderBench – Automated Evaluation of Collaboration based on Cohesion and Dialogism. International Journal of Computer-Supported Collaborative Learning, 10(4), 395–423. doi: 10.1007/s11412-015-9226-y
URI: 10.1007/s11412-015-9226-y
http://hdl.handle.net/1820/6483
Appears in Collections:1. RAGE Publications

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