Minimally Invasive Tutoring of Complex Physics Problem Solving
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
ITS '92 Proceedings of the Second International Conference on Intelligent Tutoring Systems
Using linguistic cues for the automatic recognition of personality in conversation and text
Journal of Artificial Intelligence Research
Cohesion Relationships in Tutorial Dialogue as Predictors of Affective States
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Automatic evaluation of learner self-explanations and erroneous responses for dialogue-based ITSs
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
Predicting learner's project performance with dialogue features in online q&a discussions
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
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We explored the possibility of predicting the quality of student answers (error-ridden, vague, partially-correct, and correct) to tutor questions by examining their linguistic patterns in 50 tutoring sessions with expert human tutors As an alternative to existing computational linguistic methods that focus on domain-dependent content words (e.g., velocity, RAM, speed) in interpreting a student's response, we focused on function words (e.g., I, you, but) and domain-independent content words (e.g., think, because, guess) Proportional incidence of these word categories in over 6,000 student responses to tutor questions was automatically computed using Linguistic Inquiry and Word Count (LIWC), a computer program for analyzing text Multiple regression analyses indicated that two parameter models consisting of pronouns (e.g., I, they, those) and discrepant terms (e.g., should, could, would) were effective in predicting the conceptual quality of student responses Furthermore, the classification accuracy of discriminant functions derived from the domain-independent LIWC features competed with conventional domain-dependent assessment methods We discuss the possibility of a composite assessment algorithm that focuses on both domain-dependent and domain-independent words for dialogue-based ITSs.