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Journals and Conferences
Data Set Used
A corpus of reflective tutorial dialogs was tagged for cohesive relationships between student and tutor. We describe our tagging scheme, and show that certain cohesive features of tutoring dialog are correlated with learning in our corpus. In particular, our semantic cohesive relationship tags are significant predictors of learning, while our lexical tag is… (More)
In this paper we examine whether the student-to-tutor convergence of lexical and speech features is a useful predictor of learning in a corpus of spoken tutorial dialogs. This possibility is raised by the Interactive Alignment Theory, which suggests a connection between convergence of speech features and the amount of semantic alignment between partners in… (More)
Two measures of lexical cohesion were developed and applied to a corpus of human-computer tutoring dialogs. For both measures, the amount of cohesion in the tutoring dialog was found to be significantly correlated to learning for students with below-mean pretest scores, but not for those with above-mean pre-test scores, even though both groups had similar… (More)
We use language technology to develop corpus measures of lexical and acoustic/prosodic convergence. We show that these measures successfully discriminate randomized from naturally ordered data, and demonstrate both lexical and acoustic/prosodic convergence in our corpus of human/human tutoring dialogs.
We examine correlations between dialogue characteristics and learning in two corpora of spoken tutoring dialogues: a human-human corpus and a human-computer corpus, both of which have been manually annotated with dialogue acts relative to the tutoring domain. The results from our human-computer corpus show that the presence of student utterances that… (More)
Experimental research has shown that human users will converge with dialog systems along many dimensions of speech, including those of acoustic/prosodic features and lexical choice. Other results suggest that speech convergence may provide a variety of benefits to spoken dialog systems, such as an improved user model, increased ease of use, improved… (More)
A previously reported measure of dialog cohesion was extended to measure cohesion by counting semantic similarity (the repetition of meaning) as well as lexical reiteration (the repetition of words) cohesive ties. Adding semantic similarity ties improved the algorithm's correlation with learning among high pre-testers in one of our corpora of tutoring… (More)
In this work we hypothesize that giving students a reflective reading after spoken dialog tutoring in qualitative physics will improve learning. The reading is designed to help students compare similar aspects of previously tutored problems, and to abstract their commonalities. We also hypothesize that student motivation will affect how well the text is… (More)
A Landscape Model analysis, adopted from the text processing literature, was run on transcripts of tutoring sessions, and a technique developed to count the occurrence of key physics points in the resulting connection matrices. This point-count measure was found to be well correlated with learning.
We apply a previously reported measure of dialog cohesion to a corpus of spoken tutoring dialogs in which motivation was measured. We find that cohesion significantly predicts changes in student motivation, as measured with a modified MSLQ instrument. This suggests that non-intrusive dialog measures can be used to measure motivation during tutoring.