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http://hdl.handle.net/10609/123146
Title: A granularity-based intelligent tutoring system for zooarchaeology
Author: Subirats Maté, Laia
Pérez, Leopoldo
Hernández, Cristo
Fort, Santiago
Gómez Moñivas, Sacha
Others: Universitat Rovira i Virgili
Universidad de La Laguna
Universidad Autónoma de Madrid
Eurecat, Centre Tecnològic de Catalunya
Universitat Oberta de Catalunya (UOC)
Keywords: supervised learning
zooarchaeology
intelligent tutoring system
Issue Date: 18-Nov-2019
Publisher: Applied Sciences
Citation: Subirats, L., Pérez, L., Hernández, C., Fort, S. & Gomez-Moñivas, S. (2019). A granularity-based intelligent tutoring system for zooarchaeology. Applied Sciences, 9(22), 1-17. doi: 10.3390/app9224960
Project identifier: info:eu-repo/grantAgreement/2017-1-ES01-KA203-038266
info:eu-repo/grantAgreement/TIN2015-72682-EXP
Also see: https://doi.org/10.3390/app9224960
Abstract: This paper presents a tutoring system which uses three different granularities for helping students to classify animals from bone fragments in zooarchaeology. The 3406 bone remains, which have 64 attributes, were obtained from the excavation of the Middle Palaeolithic site of El Salt (Alicante, Spain). The coarse granularity performs a five-class prediction, the medium a twelve-class prediction, and the fine a fifteen-class prediction. In the coarse granularity, the results show that the first 10 most relevant attributes for classification are width, bone, thickness, length, bone fragment, anatomical group, long bone circumference, X, Y, and Z. Based on those results, a user-friendly interface of the tutor has been built in order to train archaeology students to classify new remains using the coarse granularity. A pilot has been performed in the 2019 excavation season in Abric del Pastor (Alicante, Spain), where the automatic tutoring system was used by students to classify 51 new remains. The pilot experience demonstrated the usefulness of the tutoring system both for students when facing their first classification activities and also for seniors since the tutoring system gives them valuable clues for helping in difficult classification problems.
Language: English
URI: http://hdl.handle.net/10609/123146
ISSN: 2076-3417MIAR
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