Analyzing collaborative knowledge construction: multiple methods for integrated understanding
Computers & Education - Documenting collaborative interactions: Issues and approaches
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
A redundancy-based method for the extraction of relation instances from the Web
International Journal of Human-Computer Studies
An exploration of tool support for categorical coding
ICLS'08 Proceedings of the 8th international conference on International conference for the learning sciences - Volume 1
Content analysis: What are they talking about?
Computers & Education - Methodological issue in researching CSCL
Computers & Education - Methodological issue in researching CSCL
Effects of representational guidance on domain specific reasoning in CSCL
Computers in Human Behavior
Drawing-Based modeling for early science education
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
CRIWG'12 Proceedings of the 18th international conference on Collaboration and Technology
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Research suggests that providing others with elaborated explanations is more beneficial for learning than receiving explanations (e.g., Webb, 1989). Applied to chat communication in a collaborative inquiry learning environment, we would expect that in a dyad learners with more domain-related contributions than their partners would learn more. In the paper we develop a method to examine the relation between domain-related chats and learning outcome for intuitive knowledge. We describe how we automatically extract domain-related messages, and score them based on domain-orientedness. The analysis confirms that there is a positive relation between a high score on domain-related chats and the learning improvement as measured by the difference between a post-test and a pre-test on intuitive knowledge.