Dialogue Modes in Expert Tutoring
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
CycleTalk: Data Driven Design of Support for Simulation Based Learning
International Journal of Artificial Intelligence in Education
Automatic recognition of personality in conversation
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
The lie detector: explorations in the automatic recognition of deceptive language
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Finding deceptive opinion spam by any stretch of the imagination
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Detecting players personality behavior with any effort of concealment
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part II
How do they do it? investigating dialogue moves within dialogue modes in expert human tutoring
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
Seeing through deception: a computational approach to deceit detection in written communication
EACL 2012 Proceedings of the Workshop on Computational Approaches to Deception Detection
Hi-index | 0.00 |
In a corpus of expert tutoring dialogue, conversation that is considered to be "off topic" (non-pedagogical) according to a previous coding scheme is explored for its value in tutoring dynamics. Using the Linguistic Inquiry and Word Count (LIWC) tool, phases of tutoring categorized as "off topic" were compared with interactive problem solving phases to explore how the two differ on the emotional, psychological, and topical dimensions analyzed by LIWC. The results suggest that conversation classified as "off topic" serves as motivation and broad pedagogy in tutoring. These findings can be used to orient future research on "off topic" conversation, and help to make sense of both previous coding schemes and noisy data sets.