Articles producció científica> Psicologia

An Intelligent Approach Using Machine Learning Techniques to Predict Flow in People

  • Identification data

    Identifier: imarina:9295664
    Handle: https://hdl.handle.net/20.500.11797/imarina9295664
    Authors:
    Pegalajar, M CRuiz, L G BPerez-Moreiras, EBoada-Grau, JSerrano-Fernandez, M J
    Abstract:
    The goal of this study is to estimate the state of consciousness known as Flow, which is associated with an optimal experience and can indicate a person’s efficiency in both personal and professional settings. To predict Flow, we employ artificial intelligence techniques using a set of variables not directly connected with its construct. We analyse a significant amount of data from psychological tests that measure various personality traits. Data mining techniques support conclusions drawn from the psychological study. We apply linear regression, regression tree, random forest, support vector machine, and artificial neural networks. The results show that the multi-layer perceptron network is the best estimator, with an MSE of 0.007122 and an accuracy of 88.58%. Our approach offers a novel perspective on the relationship between personality and the state of consciousness known as Flow.

  • Others:

    Author, as appears in the article.: Pegalajar, M C; Ruiz, L G B; Perez-Moreiras, E; Boada-Grau, J; Serrano-Fernandez, M J
    Department: Psicologia
    URV's Author/s: Boada Grau, Joan / Serrano Fernandez, Maria Jose
    Keywords: Psychology Neural-networks Machine learning Flow Data mining Artificial neural networks work scale satisfaction psychology model flow data mining artificial neural networks
    Abstract: The goal of this study is to estimate the state of consciousness known as Flow, which is associated with an optimal experience and can indicate a person’s efficiency in both personal and professional settings. To predict Flow, we employ artificial intelligence techniques using a set of variables not directly connected with its construct. We analyse a significant amount of data from psychological tests that measure various personality traits. Data mining techniques support conclusions drawn from the psychological study. We apply linear regression, regression tree, random forest, support vector machine, and artificial neural networks. The results show that the multi-layer perceptron network is the best estimator, with an MSE of 0.007122 and an accuracy of 88.58%. Our approach offers a novel perspective on the relationship between personality and the state of consciousness known as Flow.
    Thematic Areas: Management information systems Information systems Computer science, theory & methods Computer science, information systems Computer science, artificial intelligence Computer science applications Ciencias sociales Artificial intelligence
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: mariajose.serrano@urv.cat joan.boada@urv.cat
    Author identifier: 0000-0003-0363-5522 0000-0002-1907-6887
    Record's date: 2024-11-23
    Papper version: info:eu-repo/semantics/publishedVersion
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Big Data And Cognitive Computing. 7 (2): 67-
    APA: Pegalajar, M C; Ruiz, L G B; Perez-Moreiras, E; Boada-Grau, J; Serrano-Fernandez, M J (2023). An Intelligent Approach Using Machine Learning Techniques to Predict Flow in People. Big Data And Cognitive Computing, 7(2), 67-. DOI: 10.3390/bdcc7020067
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2023
    Publication Type: Journal Publications

  • Keywords:

    Artificial Intelligence,Computer Science Applications,Computer Science, Artificial Intelligence,Computer Science, Information Systems,Computer Science, Theory & Methods,Information Systems,Management Information Systems
    Psychology
    Neural-networks
    Machine learning
    Flow
    Data mining
    Artificial neural networks
    work
    scale
    satisfaction
    psychology
    model
    flow
    data mining
    artificial neural networks
    Management information systems
    Information systems
    Computer science, theory & methods
    Computer science, information systems
    Computer science, artificial intelligence
    Computer science applications
    Ciencias sociales
    Artificial intelligence

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