Articles producció científica> Història i Història de l'Art

A Granularity-Based Intelligent Tutoring System for Zooarchaeology

  • Identification data

    Identifier: imarina:6013602
    Handle: http://hdl.handle.net/20.500.11797/imarina6013602
  • Authors:

    Sacha, Gomez-Monivas
    Fort, Santiago
    Hernandez, Cristo
    Perez, Leopoldo
    Subirats, Laia
  • Others:

    Author, as appears in the article.: Sacha, Gomez-Monivas Fort, Santiago Hernandez, Cristo Perez, Leopoldo Subirats, Laia
    Department: Història i Història de l'Art
    URV's Author/s: PÉREZ LUIS, LEOPOLDO JESUS
    Keywords: Zooarchaeology Supervised Learning Patterns Intelligent Tutoring System El Salt ARCHAEOLOGICAL DATA
    Abstract: 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.
    Thematic Areas: 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
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: leopoldoj.perez@urv.cat
    Author identifier: 0000-0002-5622-368X
    ISSN: 20763417
    Record's date: 2020-07-24
    Papper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: https://www.mdpi.com/2076-3417/9/22/4960
    Licence document URL: http://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: APPLIED SCIENCES-BASEL. (ISSN/ISBN: 20763417). 9(22):
    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
    Article's DOI: 10.3390/app9224960
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2019
    Publication Type: Journal Publications
  • Keywords:

    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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