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A Granularity-Based Intelligent Tutoring System for Zooarchaeology

  • Dades identificatives

    Identificador: imarina:6013602
    Autors:
    Sacha, Gomez-MonivasFort, SantiagoHernandez, CristoPerez, LeopoldoSubirats, Laia
    Resum:
    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.
  • Altres:

    Autor segons l'article: Sacha, Gomez-Monivas Fort, Santiago Hernandez, Cristo Perez, Leopoldo Subirats, Laia
    Departament: Història i Història de l'Art
    Autor/s de la URV: PÉREZ LUIS, LEOPOLDO JESUS
    Paraules clau: Zooarchaeology Supervised Learning Patterns Intelligent Tutoring System El Salt ARCHAEOLOGICAL DATA
    Resum: 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.
    Àrees temàtiques: 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
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    Adreça de correu electrònic de l'autor: leopoldoj.perez@urv.cat
    Identificador de l'autor: 0000-0002-5622-368X
    ISSN: 20763417
    Data d'alta del registre: 2020-07-24
    Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
    Enllaç font original: https://www.mdpi.com/2076-3417/9/22/4960
    Referència a l'article segons font original: APPLIED SCIENCES-BASEL. (ISSN/ISBN: 20763417). 9(22):
    Referència 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 Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    DOI de l'article: 10.3390/app9224960
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2019
    Tipus de publicació: Journal Publications
  • Paraules clau:

    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
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