The Architecture of Why2-Atlas: A Coach for Qualitative Physics Essay Writing

VanLehn, Kurt; Jordan, Pamela W.; Rosé, Carolyn Penstein; Bhembe, Dumisizwe; Böttner, Michael; Gaydos, Andy; Makatchev, Maxim; Pappuswamy, Umarani; Ringenberg, Michael A.; Roque, Antonio; Siler, Stephanie A.; Srivastava, R. C.

doi:10.1007/3-540-47987-2_20

Cited by 134 publications

(83 citation statements)

References 8 publications

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“…The effect of dialogue-based interaction on learning is further supported by studies that show that prompting students with little or no content in the tutoring interface is associated with an increase in the learning curves and that forcing them to generate words rather than simply reading them promotes subsequent recall of those words (Slamecka & Graf, 1978). There are at least two successful tutors that utilize conversational interface; Autotutor (A. C. Graesser et al, 2004) -a computer literacy tutor that simulates conversational dialogue and Atlas Andes and Why2 (Gertner & VanLehn, 2000;VanLehn K. et al, 2002) -a set of physics tutors that attempt to comprehend natural language and plan dialogue moves.…”

Section: Introductionmentioning

confidence: 98%

“…Often, the intermediate cognitive steps are first identified using empirical methods such as cognitive task analysis. Some cognitive intelligent tutoring systems (CITS) use language based interfaces (Evens et al, 2001; Ros'e C., Litman D., Behembe D., Forbes K., & VanLehn K., 2003; VanLehn K., Jordan P. W., Ros'e C., & Wilson R., 2002). Both standard CITS 8 and those with language-based interfaces (Corbett, McLaughlin, & Scarpinatto, 2000;A.…”

Section: Introductionmentioning

confidence: 99%

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A natural language intelligent tutoring system for training pathologists: implementation and evaluation

Saadawi

Tseytlin

Legowski

et al. 2007

Adv in Health Sci Educ

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Introduction-We developed and evaluated a Natural Language Interface (NLI) for an Intelligent Tutoring System (ITS) in Diagnostic Pathology. The system teaches residents to examine pathologic slides and write accurate pathology reports while providing immediate feedback on errors they make in their slide review and diagnostic reports. Residents can ask for help at any point in the case, and will receive context-specific feedback.

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

A natural language intelligent tutoring system for training pathologists: implementation and evaluation

Saadawi

Tseytlin

Legowski

et al. 2007

Adv in Health Sci Educ

View full text Add to dashboard Cite

show abstract

“…Another approach is to employ an open-domain parser in combination with domain-specific knowledge. CARMEL (Rosé, 2000) is a natural language understanding component that has been used in multiple dialogue systems (Zinn et al, 2000;Litman, 2004;VanLehn et al, 2002). CARMEL uses a semantic interpretation framework that performs semantic interpretation with semantic constructor functions during syntactic parsing.…”

Section: Language Understanding In Tutorial Dialogue Systemsmentioning

confidence: 99%

“…Today's tutorial dialogue systems engage in natural language dialogue in support of tasks such as solving qualitative physics problems (VanLehn et al, 2002), understanding computer architecture and physics (Graesser et al, 2004), and predicting behavior of electrical circuits (Dzikovska et al, 2011). Although these systems differ in many ways, they have an important commonality: in order to semantically interpret user dialogue utterances, these systems ground the utterances in a fixed domain description that is an integral part of the engineered system.…”

Section: Introductionmentioning

confidence: 99%

Semantic Grounding in Dialogue for Complex Problem Solving

Boyer

2015

Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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Dialogue systems that support users in complex problem solving must interpret user utterances within the context of a dynamically changing, user-created problem solving artifact. This paper presents a novel approach to semantic grounding of noun phrases within tutorial dialogue for computer programming. Our approach performs joint segmentation and labeling of the noun phrases to link them to attributes of entities within the problem-solving environment. Evaluation results on a corpus of tutorial dialogue for Java programming demonstrate that a Conditional Random Field model performs well, achieving an accuracy of 89.3% for linking semantic segments to the correct entity attributes. This work is a step toward enabling dialogue systems to support users in increasingly complex problem-solving tasks.

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“…ITSPOKE is a fully automated spoken dialogue tutoring system that is built on top of the Why2-Atlas text-based tutoring system (VanLehn et al, 2002).…”

Section: Itspoke: Original and Wizarded System Versionsmentioning

confidence: 99%

Designing and evaluating a wizarded uncertainty-adaptive spoken dialogue tutoring system

Forbes-Riley

Litman

2011

Computer Speech & Language

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We describe the design and evaluation of two different dynamic student uncertainty adaptations in wizarded versions of a spoken dialogue tutoring system. The two adaptive systems adapt to each student turn based on its uncertainty, after an unseen human "wizard" performs speech recognition and natural language understanding and annotates the turn for uncertainty. The design of our two uncertainty adaptations is based on a hypothesis in the literature that uncertainty is an "opportunity to learn"; both adaptations use additional substantive content to respond to uncertain turns, but the two adaptations vary in the complexity of these responses. The evaluation of our two uncertainty adaptations represents one of the first controlled experiments to investigate whether substantive dynamic responses to student affect can significantly improve performance in computer tutors. To our knowledge we are the first study to show that dynamically responding to uncertainty can significantly improve learning during computer tutoring. We also highlight our ongoing evaluation of our uncertainty-adaptive systems with respect to other important performance metrics, and we discuss how our corpus can be used by the wider computer speech and language community as a linguistic resource supporting further research on effective affect-adaptive spoken dialogue systems in general.

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The Architecture of Why2-Atlas: A Coach for Qualitative Physics Essay Writing

Cited by 134 publications

References 8 publications

A natural language intelligent tutoring system for training pathologists: implementation and evaluation

A natural language intelligent tutoring system for training pathologists: implementation and evaluation

Semantic Grounding in Dialogue for Complex Problem Solving

Designing and evaluating a wizarded uncertainty-adaptive spoken dialogue tutoring system

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