N06-1034 models of system performance in our spoken dialogue tutoring system . We represent performance
E06-1037 annotated corpus of 20 human-computer spoken dialogue tutoring ses - sions . Each session consists
N07-1035 annotated corpus of human-computer spoken dialogue tutoring sessions . The fixed-policy corpus
E06-1037 dialogue features are important to a spoken dialogue tutoring system . Our experiments show
W09-3940 hypothesis in the context of a wizarded spoken dialogue tutoring system , where student learning
N06-1034 predictive models of performance in our spoken dialogue tutoring system . Although to our knowledge
N06-1035 user state in the domain of a spoken dialogue tutoring system . In addition , we also
W04-2326 more . 4 Annotation Scheme In our spoken dialogue tutoring corpora , student emotional states
N04-1026 Annotating Student Emotion In our spoken dialogue tutoring corpus , student emotional states
W04-2326 described next . Our Human-Human Spoken Dialogue Tutoring Corpus contains spoken dialogues
W11-2024 students and our fully automated spoken dialogue tutoring system , ITSPOKE .2 Two sets
P11-3017 which can be implemented in a spoken dialogue tutoring system . The goal would be to
N06-1034 recent years the development of spoken dialogue tutoring systems has become more prevalent
N03-2018 to demonstrate that enhancing a spoken dialogue tutoring system to automatically predict
N06-1034 usefulness of these models . 2 Spoken Dialogue Tutoring Corpora ITSPOKE ( Intelligent
N06-1034 user satisfaction , such as our spoken dialogue tutoring system . We thus use 2 metrics
N12-1010 evaluating our uncertainty - adaptive spoken dialogue tutoring system , ITSPOKE ( Intelligent
N03-2018 and Aleven , 2002 ) . Building spoken dialogue tutoring systems has great potential benefit
N06-1035 annotated corpus of 20 human-computer spoken dialogue tutoring sessions ( for our work we use
hide detail