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Digital Competence of Future Secondary School Teachers: Differences According to Gender, Age, and Branch of Knowledge

Author

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  • David Jiménez-Hernández

    (Center for Operational Research, Miguel Hernández University, 03202 Elche, Spain)

  • Víctor González-Calatayud

    (Center for Operational Research, Miguel Hernández University, 03202 Elche, Spain)

  • Ana Torres-Soto

    (Center for Operational Research, Miguel Hernández University, 03202 Elche, Spain)

  • Asunción Martínez Mayoral

    (Center for Operational Research, Miguel Hernández University, 03202 Elche, Spain)

  • Javier Morales

    (Center for Operational Research, Miguel Hernández University, 03202 Elche, Spain)

Abstract

The development of related technological skills in secondary students is perceived as unachievable if the teachers do not have enough technological expertise to guide their students. This study was based on investigating digital competence on the population of graduate students undertaking a Master’s degree in Education to train as teachers for the secondary educational level. The study made it possible to conclude, on the one hand, the homogeneity of university degrees within the scope of the Bologna Plan, with respect to mean levels of digital training. On the other hand, more exhaustively, differences have come to light concerning specific training on the different digital competence areas in the DigComp evaluation system, related to gender, branch of knowledge, age, and by considering the self-perception of individuals on their own capabilities in everyday technological issues. Consequently, the need for incorporating an ICT syllabus into the subjects covered by a Master’s in education has been highlighted, as well as promoting females, older people, and nontechnological degrees. Digital training on critical competence areas for teaching, such as the creation of digital content, suffers from major shortcomings and should also be promoted.

Suggested Citation

  • David Jiménez-Hernández & Víctor González-Calatayud & Ana Torres-Soto & Asunción Martínez Mayoral & Javier Morales, 2020. "Digital Competence of Future Secondary School Teachers: Differences According to Gender, Age, and Branch of Knowledge," Sustainability, MDPI, vol. 12(22), pages 1-16, November.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:22:p:9473-:d:444962
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    References listed on IDEAS

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    1. Martins, Thiago G. & Simpson, Daniel & Lindgren, Finn & Rue, Håvard, 2013. "Bayesian computing with INLA: New features," Computational Statistics & Data Analysis, Elsevier, vol. 67(C), pages 68-83.
    2. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    3. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
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    Cited by:

    1. Camilo A. Velandia Rodriguez & Andres F. Mena-Guacas & Sergio Tobón & Eloy López-Meneses, 2022. "Digital Teacher Competence Frameworks Evolution and Their Use in Ibero-America up to the Year the COVID-19 Pandemic Began: A Systematic Review," IJERPH, MDPI, vol. 19(24), pages 1-16, December.
    2. Marcos Cabezas-González & Sonia Casillas-Martín & Francisco José García-Peñalvo, 2021. "The Digital Competence of Pre-Service Educators: The Influence of Personal Variables," Sustainability, MDPI, vol. 13(4), pages 1-14, February.
    3. Marcos Cabezas-González & Sonia Casillas-Martín & Ana García-Valcárcel Muñoz-Repiso, 2021. "Basic Education Students’ Digital Competence in the Area of Communication: The Influence of Online Communication and the Use of Social Networks," Sustainability, MDPI, vol. 13(8), pages 1-16, April.
    4. Yu Zhao & María Cruz Sánchez Gómez & Ana María Pinto Llorente & Liping Zhao, 2021. "Digital Competence in Higher Education: Students’ Perception and Personal Factors," Sustainability, MDPI, vol. 13(21), pages 1-17, November.

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