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Learning the Features Used To Decide How to Teach

Authors:

Emma BrunskillAuthors Info & Claims

L@S '15: Proceedings of the Second (2015) ACM Conference on Learning @ Scale

Pages 421 - 424

https://doi.org/10.1145/2724660.2728707

Published: 14 March 2015 Publication History

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Abstract

As a step towards scaling personalized instruction, we seek to automatically identify the key features of the interactive learning process teachers use to select the next activity when teaching a single student. Such features could both inform computational student models designed to facilitate instructional decisions, and help enable automated self-improving teaching systems that leverage this identified feature set. We present preliminary results that a very small set of features is almost as good as a much larger set of features at predicting human tutor decisions when teaching students about histograms.

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Kaplan, J. J., Gabrosek, J. G., Curtiss, P., and Malone, C. Investigating student understanding of histograms. Journal of Statistics Education 22, 2 (2014).

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Cited By

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Kim J(2024)Types of teacher-AI collaboration in K-12 classroom instruction: Chinese teachers’ perspectiveEducation and Information Technologies10.1007/s10639-024-12523-329:13(17433-17465)Online publication date: 23-Feb-2024
https://doi.org/10.1007/s10639-024-12523-3
Holstein KAleven VRummel N(2020)A Conceptual Framework for Human–AI Hybrid Adaptivity in EducationArtificial Intelligence in Education10.1007/978-3-030-52237-7_20(240-254)Online publication date: 30-Jun-2020
https://doi.org/10.1007/978-3-030-52237-7_20
Kokku RSundararajan SDey PSindhgatta RNitta SSengupta B(2018)Augmenting Classrooms with AI for Personalized Education2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP.2018.8461812(6976-6980)Online publication date: Apr-2018
https://doi.org/10.1109/ICASSP.2018.8461812

Index Terms

Learning the Features Used To Decide How to Teach
1. Applied computing

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Information & Contributors

Information

Published In

L@S '15: Proceedings of the Second (2015) ACM Conference on Learning @ Scale

March 2015

438 pages

ISBN:9781450334112

DOI:10.1145/2724660

General Chair:
Gregor Kiczales
University of British Columbia
,
Program Chairs:
Daniel M. Russell
Google
,
Beverly Woolf
University of Massachusets, Amherst

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.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 March 2015

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Work in progress

Funding Sources

Google focused research award.

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L@S 2015

Sponsor:

ACM Ed Board

L@S 2015: Second (2015) ACM Conference on Learning @ Scale

March 14 - 18, 2015

BC, Vancouver, Canada

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L@S '15 Paper Acceptance Rate 23 of 90 submissions, 26%;

Overall Acceptance Rate 117 of 440 submissions, 27%

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Cited By

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Kim J(2024)Types of teacher-AI collaboration in K-12 classroom instruction: Chinese teachers’ perspectiveEducation and Information Technologies10.1007/s10639-024-12523-329:13(17433-17465)Online publication date: 23-Feb-2024
https://doi.org/10.1007/s10639-024-12523-3
Holstein KAleven VRummel N(2020)A Conceptual Framework for Human–AI Hybrid Adaptivity in EducationArtificial Intelligence in Education10.1007/978-3-030-52237-7_20(240-254)Online publication date: 30-Jun-2020
https://doi.org/10.1007/978-3-030-52237-7_20
Kokku RSundararajan SDey PSindhgatta RNitta SSengupta B(2018)Augmenting Classrooms with AI for Personalized Education2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP.2018.8461812(6976-6980)Online publication date: Apr-2018
https://doi.org/10.1109/ICASSP.2018.8461812

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Recommendations

Personalized E-learning system with self-regulated learning assisted mechanisms for promoting learning performance

Effects of interactivity and instructional scaffolding on learning: Self-regulation in online video-based environments

Pedagogical Agent Support and Its Relationship to Learners’ Self-regulated Learning Strategy Use with an Intelligent Tutoring System