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    Por favor, use este identificador para citar o enlazar este ítem:https://uvadoc.uva.es/handle/10324/49171

    Título
    EFAR-MMLA: An evaluation framework to assess and report generalizability of machine learning models in MMLA
    Autor
    Chejara, Pankaj
    Prieto Santos, Luis Pablo
    Ruiz Calleja, AdolfoAutoridad UVA
    Rodríguez Triana, María JesúsAutoridad UVA Orcid
    Kant Shankar, Shashi
    Kasepalu, Reet
    Año del Documento
    2021
    Editorial
    MDPI
    Descripción
    Producción Científica
    Documento Fuente
    Sensors, 2021, vol. 21, n. 8, 2863
    Abstract
    Multimodal Learning Analytics (MMLA) researchers are progressively employing machine learning (ML) techniques to develop predictive models to improve learning and teaching practices. These predictive models are often evaluated for their generalizability using methods from the ML domain, which do not take into account MMLA’s educational nature. Furthermore, there is a lack of systematization in model evaluation in MMLA, which is also reflected in the heterogeneous reporting of the evaluation results. To overcome these issues, this paper proposes an evaluation framework to assess and report the generalizability of ML models in MMLA (EFAR-MMLA). To illustrate the usefulness of EFAR-MMLA, we present a case study with two datasets, each with audio and log data collected from a classroom during a collaborative learning session. In this case study, regression models are developed for collaboration quality and its sub-dimensions, and their generalizability is evaluated and reported. The framework helped us to systematically detect and report that the models achieved better performance when evaluated using hold-out or cross-validation but quickly degraded when evaluated across different student groups and learning contexts. The framework helps to open up a “wicked problem” in MMLA research that remains fuzzy (i.e., the generalizability of ML models), which is critical to both accumulating knowledge in the research community and demonstrating the practical relevance of these techniques.
    Palabras Clave
    Multimodal learning
    Aprendizaje multimodal
    Machine learning
    Aprendizaje automático
    ISSN
    1424-8220
    Revisión por pares
    SI
    DOI
    10.3390/s21082863
    Patrocinador
    Fondo Europeo de Desarrollo Regional - Agencia Nacional de Investigación (grants TIN2017-85179-C3-2-R and TIN2014-53199-C3-2-R)
    Fondo Europeo de Desarrollo Regional - Junta de Castilla y León (grant VA257P18)
    Comisión Europea (grant 588438-EPP-1- 2017-1-EL-EPPKA2-KA)
    Version del Editor
    https://www.mdpi.com/1424-8220/21/8/2863
    Propietario de los Derechos
    © 2021 MDPI
    Idioma
    eng
    URI
    https://uvadoc.uva.es/handle/10324/49171
    Tipo de versión
    info:eu-repo/semantics/publishedVersion
    Derechos
    openAccess
    Aparece en las colecciones
    • GSIC - Artículos de revista [14]
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    Attribution-NonCommercial-NoDerivatives 4.0 InternacionalLa licencia del ítem se describe como Attribution-NonCommercial-NoDerivatives 4.0 Internacional

    Universidad de Valladolid

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