Articles producció científica> Història i Història de l'Art

A Granularity-Based Intelligent Tutoring System for Zooarchaeology

  • Datos identificativos

    Identificador: imarina:6013602
    Autores:
    Sacha, Gomez-MonivasFort, SantiagoHernandez, CristoPerez, LeopoldoSubirats, Laia
    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.
  • Otros:

    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
    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
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    DOI del artículo: 10.3390/app9224960
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2019
    Tipo de publicación: Journal Publications
  • Palabras clave:

    Chemistry, Multidisciplinary,Computer Science Applications,Engineering (Miscellaneous),Engineering, Multidisciplinary,Fluid Flow and Transfer Processes,Instrumentation,Materials Science (Miscellaneous),Materials Science, Multidisciplinary,Physics, Applied,Process Chemistry and Technology
    Zooarchaeology
    Supervised Learning
    Patterns
    Intelligent Tutoring System
    El Salt
    ARCHAEOLOGICAL DATA
    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
    20763417
  • Documentos:

  • Cerca a google

    Search to google scholar