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Using Context to Predict the Purpose of Argumentative Writing Revisions.

, and . HLT-NAACL, page 1424-1430. The Association for Computational Linguistics, (2016)

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When Does Disengagement Correlate with Learning in Spoken Dialog Computer Tutoring?, and . AIED, volume 6738 of Lecture Notes in Computer Science, page 81-89. Springer, (2011)Illustrations or Graphs: Some Students Benefit from One over the Other., and . AIED, volume 7926 of Lecture Notes in Computer Science, page 746-749. Springer, (2013)Uncertainty Corpus: Resource to Study User Affect in Complex Spoken Dialogue Systems., , , and . LREC, European Language Resources Association, (2008)Exploiting Word-level Features for Emotion Prediction., , and . SLT, page 110-113. IEEE, (2006)The relative impact of student affect on performance models in a spoken dialogue tutoring system., , and . User Model. User-Adapt. Interact., 18 (1-2): 11-43 (2008)Automatic Summarization of Student Course Feedback., , , and . CoRR, (2018)Using Discourse Signals for Robust Instructor Intervention Prediction., , , and . CoRR, (2016)When Does Disengagement Correlate with Performance in Spoken Dialog Computer Tutoring?, and . Int. J. Artif. Intell. Educ., 22 (1-2): 39-58 (2013)Iterative Design and Classroom Evaluation of Automated Formative Feedback for Improving Peer Feedback Localization., , and . Int. J. Artif. Intell. Educ., 27 (3): 582-622 (2017)Designing and evaluating a wizarded uncertainty-adaptive spoken dialogue tutoring system., and . Comput. Speech Lang., 25 (1): 105-126 (2011)