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

Publication date

2023-06-26

Authors

Alpizar Chacon, IsaacORCID 0000-0002-6931-9787ISNI 0000000506317436
Sosnovsky, SergeyISNI 0000000352729779
Brusilovsky, Peter

Editors

Wang, Ning
Rebolledo-Mendez, Genaro
Matsuda, Noboru
Santos, Olga C.
Dimitrova, Vania

Advisors

Supervisors

Document Type

Part of book
Open Access logo

License

taverne

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.

Keywords

Knowledge Extraction, Learning Curves, Textbooks, Taverne, Theoretical Computer Science, General Computer Science

Citation

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. 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