Autor según el artículo: Sacha, Gomez-Monivas Fort, Santiago Hernandez, Cristo Perez, Leopoldo Subirats, Laia

Departamento: Història i Història de l'Art

Autor/es de la URV: PÉREZ LUIS, LEOPOLDO JESUS

Palabras clave: Zooarchaeology Supervised Learning Patterns Intelligent Tutoring System El Salt ARCHAEOLOGICAL DATA

Resumen: Featured Application This study creates an intelligent tutoring system in archaeology for helping students in specialized tasks that require analysis of huge amounts of data. The method proposed here implies the application of classification algorithms that must be trained with a complete data set in order to give accurate results. We have tested our method by developing an intelligent tutoring system in the field of zooarchaeology. 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.

Áreas temáticas: Process Chemistry and Technology Physics, Applied Materials Science, Multidisciplinary Materials Science (Miscellaneous) Instrumentation General Materials Science General Engineering Fluid Flow and Transfer Processes Engineering (Miscellaneous) Computer Science Applications Chemistry, Multidisciplinary

Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/

Direcció de correo del autor: leopoldoj.perez@urv.cat

Identificador del autor: 0000-0002-5622-368X

ISSN: 20763417

Fecha de alta del registro: 2020-07-24

Versión del articulo depositado: info:eu-repo/semantics/publishedVersion

Enlace a la fuente original: https://www.mdpi.com/2076-3417/9/22/4960

URL Documento de licencia: http://repositori.urv.cat/ca/proteccio-de-dades/

Referencia al articulo segun fuente origial: APPLIED SCIENCES-BASEL. (ISSN/ISBN: 20763417). 9(22):

Referencia de l'ítem segons les normes APA: Subirats, Laia; Perez, Leopoldo; Hernandez, Cristo; Fort, Santiago; Sacha, Gomez-Monivas (2019). A Granularity-Based Intelligent Tutoring System for Zooarchaeology. APPLIED SCIENCES-BASEL, 9(22), -. DOI: 10.3390/app9224960

DOI del artículo: 10.3390/app9224960

Entidad: Universitat Rovira i Virgili

Año de publicación de la revista: 2019

Tipo de publicación: Journal Publications