Measuring the Quality of Domain Models Extracted from Textbooks with Learning Curves Analysis

Isaac Alpizar-Chacon; Sergey Sosnovsky; Peter Brusilovsky

doi:10.1007/978-3-031-36272-9_75

Measuring the Quality of Domain Models Extracted from Textbooks with Learning Curves Analysis

Isaac Alpizar-Chacon^*, Sergey Sosnovsky, Peter Brusilovsky

^*Corresponding author for this work

Sub Software Technology for Learning and Teaching

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

Abstract

This paper evaluates an automatically extracted domain model from textbooks and applies learning curve analysis to assess its ability to represent students’ knowledge and learning. Results show that extracted concepts are meaningful knowledge components with varying granularity, depending on textbook authors’ perspectives. The evaluation demonstrates the acceptable quality of the extracted domain model in knowledge modeling.

Original language	English
Title of host publication	Artificial Intelligence in Education
Subtitle of host publication	24th International Conference, AIED 2023, Tokyo, Japan, July 3–7, 2023, Proceedings
Editors	Ning Wang, Genaro Rebolledo-Mendez, Noboru Matsuda, Olga C. Santos, Vania Dimitrova
Publisher	Springer
Pages	804-809
Number of pages	6
ISBN (Print)	9783031362712
DOIs	https://doi.org/10.1007/978-3-031-36272-9_75
Publication status	Published - 26 Jun 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13916 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Funding

Funders	Funder number
Enkelejda Kasneci
Huawei
Leibniz-WissenschaftsCampus Tübingen
Ministry of Science and Culture of Lower Saxony
Ministry of Science, Research and the Arts
NSF National AI Institute	DRL 2019805
National Defense Education Program
U.S. Army DEV-COM	W912CG-19-2-0001
UIDB/00027/2020 of the Artificial Intelligence and Computer Science Laboratory
UME Academy Ltd. academy
National Science Foundation	R305A190063, 2101104, 1917713, 2138854, R305A130124, 1824257, 2215193, 2118706, IIS-0735682, 2016966, REC0241144, -2201796, 1623702, 2202506, 2013502, DGE-1842213
U.S. Department of Education
Alexander von Humboldt-Stiftung
Institute of Education Sciences	R305A210432
North Carolina State University
Horizon 2020 Framework Programme	761758, 945447
Natural Sciences and Engineering Research Council of Canada
Engineering and Physical Sciences Research Council	EP/P024289/1
Deutsche Forschungsgemeinschaft	390727645, 2064/1
Japan Society for the Promotion of Science	19H05663, 21H00898, 20K20817
National Natural Science Foundation of China	62277045
University Grants Committee	17605221
Fundação para a Ciência e a Tecnologia
Bundesministerium für Bildung und Forschung	01JD1905B, 01JD1905A
Ministry of Education	2022R1A6A1A0305295412
Research Grants Council, University Grants Committee
Innovation and Technology Commission	ITB/FBL/7026/20/P
National Research Foundation of Korea
Mitacs	13336
Ministério da Ciência, Tecnologia e Ensino Superior
Natural Science Foundation of Shandong Province	ZR 2021MF011
Ontario Ministry of Research and Innovation	124812, 344/390020/ 06.09.2021

Keywords

Knowledge Extraction
Learning Curves
Textbooks

Access to Document

10.1007/978-3-031-36272-9_75

Artificial Intelligence in EducationFinal published version, 346 KBLicence: Taverne

Cite this

Alpizar-Chacon, I., Sosnovsky, S., & Brusilovsky, P. (2023). Measuring the Quality of Domain Models Extracted from Textbooks with Learning Curves Analysis. In N. Wang, G. Rebolledo-Mendez, N. Matsuda, O. C. Santos, & V. Dimitrova (Eds.), Artificial Intelligence in Education: 24th International Conference, AIED 2023, Tokyo, Japan, July 3–7, 2023, Proceedings (pp. 804-809). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13916 LNAI). Springer. https://doi.org/10.1007/978-3-031-36272-9_75

Alpizar-Chacon, Isaac ; Sosnovsky, Sergey ; Brusilovsky, Peter. / Measuring the Quality of Domain Models Extracted from Textbooks with Learning Curves Analysis. Artificial Intelligence in Education: 24th International Conference, AIED 2023, Tokyo, Japan, July 3–7, 2023, Proceedings. editor / Ning Wang ; Genaro Rebolledo-Mendez ; Noboru Matsuda ; Olga C. Santos ; Vania Dimitrova. Springer, 2023. pp. 804-809 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "This paper evaluates an automatically extracted domain model from textbooks and applies learning curve analysis to assess its ability to represent students{\textquoteright} knowledge and learning. Results show that extracted concepts are meaningful knowledge components with varying granularity, depending on textbook authors{\textquoteright} perspectives. The evaluation demonstrates the acceptable quality of the extracted domain model in knowledge modeling.",

keywords = "Knowledge Extraction, Learning Curves, Textbooks",

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Alpizar-Chacon, I , Sosnovsky, S & Brusilovsky, P 2023, Measuring the Quality of Domain Models Extracted from Textbooks with Learning Curves Analysis. in N Wang, G Rebolledo-Mendez, N Matsuda, OC Santos & V Dimitrova (eds), Artificial Intelligence in Education: 24th International Conference, AIED 2023, Tokyo, Japan, July 3–7, 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13916 LNAI, Springer, pp. 804-809. https://doi.org/10.1007/978-3-031-36272-9_75

Measuring the Quality of Domain Models Extracted from Textbooks with Learning Curves Analysis. / Alpizar-Chacon, Isaac ; Sosnovsky, Sergey; Brusilovsky, Peter.
Artificial Intelligence in Education: 24th International Conference, AIED 2023, Tokyo, Japan, July 3–7, 2023, Proceedings. ed. / Ning Wang; Genaro Rebolledo-Mendez; Noboru Matsuda; Olga C. Santos; Vania Dimitrova. Springer, 2023. p. 804-809 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13916 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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Alpizar-Chacon I , Sosnovsky S, Brusilovsky P. Measuring the Quality of Domain Models Extracted from Textbooks with Learning Curves Analysis. In Wang N, Rebolledo-Mendez G, Matsuda N, Santos OC, Dimitrova V, editors, Artificial Intelligence in Education: 24th International Conference, AIED 2023, Tokyo, Japan, July 3–7, 2023, Proceedings. Springer. 2023. p. 804-809. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-36272-9_75