Measuring the Quality of Domain Models Extracted from Textbooks with Learning Curves Analysis
Publication date
2023-06-26
Editors
Wang, Ning
Rebolledo-Mendez, Genaro
Matsuda, Noboru
Santos, Olga C.
Dimitrova, Vania
Advisors
Supervisors
Document Type
Part of book
Metadata
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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