Talk Like an Electrician: Student Dialogue Mimicking Behavior in an Intelligent Tutoring System

Steinhauser, Natalie B.; Campbell, Gwendolyn E.; Taylor, Leanne S.; Caine, Simon; Scott, Charlie; Dzikovska, Myroslava O.; Moore, Johanna D.

doi:10.1007/978-3-642-21869-9_47

Cited by 5 publications

(3 citation statements)

References 2 publications

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“…This policy assumes that it is always better to use a content-free prompt than to reject a non-interpretable student utterance. How-ever, interpretation problems can arise from incorrect uses of terminology, and learning to speak in the language of the domain has been positively correlated with learning outcomes (Steinhauser et al, 2011). Therefore, rejecting some non-interpretable answers as incorrect could be a valid tutoring strategy (Sagae et al, 2010;Dzikovska et al, 2010a).…”

Section: Discussion and Future Workmentioning

confidence: 99%

Dealing with interpretation errors in tutorial dialogue

Dzikovska¹,

Callaway²,

Farrow³

et al. 2009

Proceedings of the SIGDIAL 2009 Conference on the 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue -

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We describe an approach to dealing with interpretation errors in a tutorial dialogue system. Allowing students to provide explanations and generate contentful talk can be helpful for learning, but the language that can be understood by a computer system is limited by the current technology. Techniques for dealing with understanding problems have been developed primarily for spoken dialogue systems in informationseeking domains, and are not always appropriate for tutorial dialogue. We present a classification of interpretation errors and our approach for dealing with them within an implemented tutorial dialogue system.

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Section: Discussion and Future Workmentioning

confidence: 99%

Dealing with interpretation errors in tutorial dialogue

Dzikovska¹,

Callaway²,

Farrow³

et al. 2009

Proceedings of the SIGDIAL 2009 Conference on the 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue -

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“…When the student hears his words (or word order) echoed in the tutor's turn, the student knows that the tutor understood what he or she said. Several studies have shown that the degree of lexical and syntactic cohesion (alignment) during tutoring predicts learning (e.g., Litman & Forbes-Riley, 2006;Steinhauser et al, 2011;Ward & Litman, 2008, 2011, in addition to potentially enhancing coordination.…”

Section: Cooperative Execution During Scaffoldingmentioning

confidence: 99%

A tutoring system that simulates the highly interactive nature of human tutoring.

Katz¹,

Albacete²

2013

Journal of Educational Psychology

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For some time, it has been clear that students who are tutored generally leam more than students who experience classroom instruction (e.g.. Bloom, 1984). Much research has been devoted to identifying features of tutorial dialogue that can explain its effectiveness, so that these features can be simulated in natural-language tutoring systems. One hypothesis is that the highly interactive nature of tutoring itself promotes leaming-that is, the interaction hypothesis. Although reasonable and agreeing with much research, the interaction hypothesis raises the question of what linguistic mechanisms are involved: that is, which features of "highly interactive" dialogues trigger what processes that are conducive to learning? Our overall strategy in the research described in this article was to inform this question by identifying co-constmcted discourse relations in tutorial dialogues whose frequency of occurrence predicts leaming, identify the context in which these relations occur, and use this knowledge to formulate decision rules to guide automated dialogues. We used Rhetorical Structure Theory to identify and tag co-constmcted discourse relations in a large corpus of physics tutoring dialogues. Our analyses suggest that the effectiveness of human tutoring might well lie in the language of tutoring itself Moreover, the types of co-constructed discourse relations that predict leaming seem to vary based on students' ability level. We describe Rimac, a natural-language tutoring system that implements an initial set of decision rules based on these analyses. These rules guide reflective dialogues about the concepts associated with physics problems. Rimac is being pilot tested in high school physics classes.Educators and policy makers in the United States have looked to educational technology as a tool to increase students' proficiency in math, science, reading, and other subject matter domains. For example, early in his administration. President Obama (2009) challenged developers of intelligent tutoring systems (ITSs) to develop "leaming software as effective as a personal tutor" (para. 19). Apparently, Obama cast this challenge a bit too late. A recent meta-analysis of research comparing the effectiveness of human tutors with state-of-the-art ITSs showed that ITSs have already nearly caught up with human tutors (VanLehn, 2011), with effect sizes {d) of 0.76 for human tutoring and 0.79 for ITSs relative to

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“…Never rejecting answers as uninterpretable can reduce student frustration. However, uninterpretable utterances often arise from incorrect uses of terminology, and learning to speak in the way expected for the domain has been positively correlated with learning outcomes [22]. The semantic interpreter provides information about the nature of interpretation failures that supports generation of targeted help messages, pointing out problematic wordings not consistent with the domain, such as "Paths cannot be broken, only components can be broken."…”

Section: Combining Semantic Interpretation and Classificationmentioning

confidence: 99%

Combining Semantic Interpretation and Statistical Classification for Improved Explanation Processing in a Tutorial Dialogue System

Dzikovska

Farrow

Moore

2013

Lecture Notes in Computer Science

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Abstract. We present an approach for combining symbolic interpretation and statistical classification in the natural language processing (NLP) component of a tutorial dialogue system. Symbolic NLP approaches support dynamic generation of context-adaptive natural language feedback, but lack robustness. In contrast, statistical classification approaches are robust to ill-formed input but provide less detail for context-specific feedback generation. We describe a system design that combines symbolic interpretation with statistical classification to support context-adaptive, dynamically generated natural language feedback, and show that the combined system significantly improves interpretation quality while retaining the adaptivity benefits of a symbolic interpreter.

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Talk Like an Electrician: Student Dialogue Mimicking Behavior in an Intelligent Tutoring System

Cited by 5 publications

References 2 publications

Dealing with interpretation errors in tutorial dialogue

Dealing with interpretation errors in tutorial dialogue

A tutoring system that simulates the highly interactive nature of human tutoring.

Combining Semantic Interpretation and Statistical Classification for Improved Explanation Processing in a Tutorial Dialogue System

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