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DeepTutor: towards macro- and micro-adaptive conversational intelligent tutoring at scale

Authors:

Vasile Rus,

Dan Stefanescu,

Nobal Niraula,

Arthur C. GraesserAuthors Info & Claims

L@S '14: Proceedings of the first ACM conference on Learning @ scale conference

Pages 209 - 210

https://doi.org/10.1145/2556325.2567885

Published: 04 March 2014 Publication History

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Abstract

We present an overview of the design of a conversational intelligent tutoring system, called DeepTutor, based on the framework of Learning Progressions. Learning Progressions capture students' successful paths towards mastery. The assumption of the proposed tutor is that by guiding instruction based on Learning Progressions, the system will be more effective (and efficient for that matter).

References

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Duschl, R.A., Schweingruber, H.A., & Shouse, A. (Eds.). (2007). Taking science to school: Learning and teaching science in grades K-8. Washington, DC: National Academy Press.

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[2]

VanLehn, K. 2006 The behavior of tutoring systems. International Journal of Artificial Intelligence in Education. 16 (3), 227--265.

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[3]

Mohan, L.; Chen, J.; and Anderson,W.A. 2009 Developing a multi-year learning progression for carbon cycling in socio-ecological systems. Journal of Research in Science Teaching, 46, 675--6.

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Cited By

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Chen YDing NZheng HLiu ZSun MZhou BSerra ESpezzano F(2024)Empowering Private Tutoring by Chaining Large Language ModelsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679665(354-364)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679665
Uglev VSychev O(2023)Synthesizing Didactic Explanatory Texts in Intelligent Tutoring Systems Based on the Information in Cognitive MapsAugmented Intelligence and Intelligent Tutoring Systems10.1007/978-3-031-32883-1_20(233-246)Online publication date: 22-May-2023
https://doi.org/10.1007/978-3-031-32883-1_20
Douali LSelmaoui SBouab W(2022)Artificial Intelligence in Education: Fears and FaithsInternational Journal of Information and Education Technology10.18178/ijiet.2022.12.7.166612:7(650-657)Online publication date: 2022
https://doi.org/10.18178/ijiet.2022.12.7.1666
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Index Terms

DeepTutor: towards macro- and micro-adaptive conversational intelligent tutoring at scale
1. Human-centered computing

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Information & Contributors

Information

Published In

L@S '14: Proceedings of the first ACM conference on Learning @ scale conference

March 2014

234 pages

ISBN:9781450326698

DOI:10.1145/2556325

General Chair:
Mehran Sahami
Stanford University, USA
,
Program Chairs:
Armando Fox
University of California, Berkeley, USA
,
Marti A. Hearst
University of California, Berkeley, USA
,
Michelene T.H. Chi
Arizona State University, USA

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 March 2014

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Poster

Conference

L@S 2014

Sponsor:

ACM Ed Board

L@S 2014: First (2014) ACM Conference on Learning @ Scale

March 4 - 5, 2014

Georgia, Atlanta, USA

Acceptance Rates

L@S '14 Paper Acceptance Rate 14 of 38 submissions, 37%;

Overall Acceptance Rate 117 of 440 submissions, 27%

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Cited By

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Chen YDing NZheng HLiu ZSun MZhou BSerra ESpezzano F(2024)Empowering Private Tutoring by Chaining Large Language ModelsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679665(354-364)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679665
Uglev VSychev O(2023)Synthesizing Didactic Explanatory Texts in Intelligent Tutoring Systems Based on the Information in Cognitive MapsAugmented Intelligence and Intelligent Tutoring Systems10.1007/978-3-031-32883-1_20(233-246)Online publication date: 22-May-2023
https://doi.org/10.1007/978-3-031-32883-1_20
Douali LSelmaoui SBouab W(2022)Artificial Intelligence in Education: Fears and FaithsInternational Journal of Information and Education Technology10.18178/ijiet.2022.12.7.166612:7(650-657)Online publication date: 2022
https://doi.org/10.18178/ijiet.2022.12.7.1666
Belfer RKochmar ESerban I(2022)Raising Student Completion Rates with Adaptive Curriculum and Contextual BanditsArtificial Intelligence in Education10.1007/978-3-031-11644-5_74(724-730)Online publication date: 27-Jul-2022
https://doi.org/10.1007/978-3-031-11644-5_74
Poulton AEliens S(2021)Explaining transformer-based models for automatic short answer gradingProceedings of the 5th International Conference on Digital Technology in Education10.1145/3488466.3488479(110-116)Online publication date: 15-Sep-2021
https://dl.acm.org/doi/10.1145/3488466.3488479
Kochmar EVu DBelfer RGupta VSerban IPineau J(2021)Automated Data-Driven Generation of Personalized Pedagogical Interventions in Intelligent Tutoring SystemsInternational Journal of Artificial Intelligence in Education10.1007/s40593-021-00267-x32:2(323-349)Online publication date: 27-Jul-2021
https://doi.org/10.1007/s40593-021-00267-x
St-Hilaire FBurns NBelfer RShayan MSmofsky AVu DFrau APotochny JFaraji FPavero VKo NChing AElkins SStepanyan AMatajova ACharlin LBengio YSerban IKochmar E(2021)A Comparative Study of Learning Outcomes for Online Learning PlatformsArtificial Intelligence in Education10.1007/978-3-030-78270-2_59(331-337)Online publication date: 12-Jun-2021
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Hall Jr. O(2020)Expanding Bloom's Two-Sigma Tutoring Theory Using Intelligent AgentsNatural Language Processing10.4018/978-1-7998-0951-7.ch015(280-301)Online publication date: 2020
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Wang LShubeck KHu X(2020)Google Service-Based CbITS Authoring Tool to Support CollaborationHCI International 2020 – Late Breaking Papers: Cognition, Learning and Games10.1007/978-3-030-60128-7_44(605-616)Online publication date: 4-Oct-2020
https://doi.org/10.1007/978-3-030-60128-7_44
Kochmar EVu DBelfer RGupta VSerban IPineau J(2020)Automated Personalized Feedback Improves Learning Gains in An Intelligent Tutoring SystemArtificial Intelligence in Education10.1007/978-3-030-52240-7_26(140-146)Online publication date: 30-Jun-2020
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