Knowledge Annotation for Intelligent Textbooks

Autor/inn/en	Wang, Mengdi; Chau, Hung; Thaker, Khushboo; Brusilovsky, Peter; He, Daqing
Titel	Knowledge Annotation for Intelligent Textbooks
Quelle	In: Technology, Knowledge and Learning, 28 (2023) 1, S.1-22 (22 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Wang, Mengdi)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2211-1662
DOI	10.1007/s10758-021-09544-z
Schlagwörter	Documentation; Electronic Publishing; Textbooks; Artificial Intelligence; Automation; Information Retrieval; Engineering; Standards; Knowledge Representation + Suchen Sie Ihr Suchwort? Dokumentation; Elektronisches Publizieren; Textbook; Text book; Schulbuch; Lehrbuch; Künstliche Intelligenz; Maschinenbau; Standard; Wissensrepräsentation
Abstract	With the increased popularity of electronic textbooks, there is a growing interest in developing a new generation of "intelligent textbooks," which have the ability to guide readers according to their learning goals and current knowledge. Intelligent textbooks extend regular textbooks by integrating machine-manipulable knowledge, and the most popular type of integrated knowledge is a list of relevant concepts mentioned in the textbooks. With these concepts, multiple intelligent operations, such as content linking, content recommendation, or student modeling, can be performed. However, existing automatic keyphrase extraction methods, even supervised ones, cannot deliver sufficient accuracy to be practically useful in this task. Manual annotation by experts has been demonstrated to be a preferred approach for producing high-quality labeled data for training supervised models. However, most researchers in the education domain still consider the concept annotation process as an ad-hoc activity rather than a carefully executed task, which can result in low-quality annotated data. Using the annotation of concepts for the "Introduction to Information Retrieval" textbook as a case study, this paper presents a knowledge engineering method to obtain reliable concept annotations. As demonstrated by the data we collected, the inter-annotator agreement gradually increased along with our procedure, and the concept annotations we produced led to better results in document linking and student modeling tasks. The contributions of our work include a validated knowledge engineering procedure, a codebook for technical concept annotation, and a set of concept annotations for the target textbook, which could be used as a gold standard in further intelligent textbook research. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01