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Using correlational topic modeling for automated topic identification in intelligent tutoring systems

Authors:

Ma. Victoria Almeda,

Neil HeffernanAuthors Info & Claims

LAK '17: Proceedings of the Seventh International Learning Analytics & Knowledge Conference

Pages 393 - 397

https://doi.org/10.1145/3027385.3027438

Published: 13 March 2017 Publication History

Abstract

Student knowledge modeling is an important part of modern personalized learning systems, but typically relies upon valid models of the structure of the content and skill in a domain. These models are often developed through expert tagging of skills to items. However, content creators in crowdsourced personalized learning systems often lack the time (and sometimes the domain knowledge) to tag skills themselves. Fully automated approaches that rely on the covariance of correctness on items can lead to effective skill-item mappings, but the resultant mappings are often difficult to interpret. In this paper we propose an alternate approach to automatically labeling skills in a crowdsourced personalized learning system using correlated topic modeling, a natural language processing approach, to analyze the linguistic content of mathematics problems. We find a range of potentially meaningful and useful topics within the context of the ASSISTments system for mathematics problem-solving.

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Cited By

Ferdinand BAviarta NJordan MPurwandari K(2024)Topic Identification of Science and Mathematics Literature Using Latent Dirichlet Allocation2024 IEEE International Conference on Artificial Intelligence and Mechatronics Systems (AIMS)10.1109/AIMS61812.2024.10512912(1-5)Online publication date: 21-Feb-2024
https://doi.org/10.1109/AIMS61812.2024.10512912
Jiménez-Macías AMuñoz-Merino PDelgado Kloos C(2021)A model to characterize exercises using probabilistic methodsNinth International Conference on Technological Ecosystems for Enhancing Multiculturality (TEEM'21)10.1145/3486011.3486523(594-599)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3486011.3486523
Moore SNguyen HStamper J(2020)Evaluating Crowdsourcing and Topic Modeling in Generating Knowledge Components from ExplanationsArtificial Intelligence in Education10.1007/978-3-030-52237-7_32(398-410)Online publication date: 30-Jun-2020
https://doi.org/10.1007/978-3-030-52237-7_32

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Published In

cover image ACM Other conferences

LAK '17: Proceedings of the Seventh International Learning Analytics & Knowledge Conference

March 2017

631 pages

ISBN:9781450348706

DOI:10.1145/3027385

General Chairs:
Alyssa Wise
New York University, USA
,
Phillip H. Winne
Simon Fraser University, Canada
,
Grace Lynch
Society for Learning Analytics Research
,
Program Chairs:
Xavier Ochoa
Escuela Superior Politecnica del Litoral, Ecuador
,
Inge Molenaar
Radboud University, The Netherlands
,
Shane Dawson
University of South Australia, Australia
,
Publications Chair:
Marek Hatala
Simon Fraser University, Canada

Copyright © 2017 ACM.

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New York, NY, United States

Publication History

Published: 13 March 2017

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National Science Foundation

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LAK '17

LAK '17: 7th International Learning Analytics and Knowledge Conference

March 13 - 17, 2017

British Columbia, Vancouver, Canada

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LAK '17 Paper Acceptance Rate 36 of 114 submissions, 32%;

Overall Acceptance Rate 236 of 782 submissions, 30%

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Cited By

Ferdinand BAviarta NJordan MPurwandari K(2024)Topic Identification of Science and Mathematics Literature Using Latent Dirichlet Allocation2024 IEEE International Conference on Artificial Intelligence and Mechatronics Systems (AIMS)10.1109/AIMS61812.2024.10512912(1-5)Online publication date: 21-Feb-2024
https://doi.org/10.1109/AIMS61812.2024.10512912
Jiménez-Macías AMuñoz-Merino PDelgado Kloos C(2021)A model to characterize exercises using probabilistic methodsNinth International Conference on Technological Ecosystems for Enhancing Multiculturality (TEEM'21)10.1145/3486011.3486523(594-599)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3486011.3486523
Moore SNguyen HStamper J(2020)Evaluating Crowdsourcing and Topic Modeling in Generating Knowledge Components from ExplanationsArtificial Intelligence in Education10.1007/978-3-030-52237-7_32(398-410)Online publication date: 30-Jun-2020
https://doi.org/10.1007/978-3-030-52237-7_32

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