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Title: Automated Extraction of Domain Models from Textbook Indexes for Developing Intelligent Tutoring Systems
Domain modeling is an important task in designing, developing, and deploying intelligent tutoring systems and other adaptive instructional systems. We focus here on the more specific task of automatically extracting a domain model from textbooks. In particular, this paper explores using multiple textbook indexes to extract a domain model for computer programming. Our approach is based on the observation that different experts, i.e., authors of intro-to-programming textbooks in our case, break down a domain in slightly different ways, and identifying the commonalities and differences can be very revealing. To this end, we present automated approaches to extracting domain models from multiple textbooks and compare the resulting common domain model with a domain model created by experts. Specifically, we use approximate string-matching approaches to increase coverage of the resulting domain model and majority voting across different textbooks to discover common domain terms related to computer programming. Our results indicate that using approximate string matching gives more accurate domain models for computer programming with increased precision and recall. By automating our approach, we can significantly reduce the time and effort required to construct high-quality domain models, making it easy to develop and deploy tutoring systems. Furthermore, we obtain a common domain model that can serve as a benchmark or skeleton that can be used broadly and adapted to specific needs by others.  more » « less
Award ID(s):
1934745
PAR ID:
10447586
Author(s) / Creator(s):
; ;
Editor(s):
Frasson, C.; Mylonas, P.; Troussas, C.
Date Published:
Journal Name:
Augmented Intelligence and Intelligent Tutoring Systems. ITS 2023. Lecture Notes in Computer Science, vol 13891
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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