acl acl2010 acl2010-227 acl2010-227-reference knowledge-graph by maker-knowledge-mining
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Author: Myroslava O. Dzikovska ; Johanna D. Moore ; Natalie Steinhauser ; Gwendolyn Campbell
Abstract: Supporting natural language input may improve learning in intelligent tutoring systems. However, interpretation errors are unavoidable and require an effective recovery policy. We describe an evaluation of an error recovery policy in the BEETLE II tutorial dialogue system and discuss how different types of interpretation problems affect learning gain and user satisfaction. In particular, the problems arising from student use of non-standard terminology appear to have negative consequences. We argue that existing strategies for dealing with terminology problems are insufficient and that improving such strategies is important in future ITS research.
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