IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i22p9473-d444962.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/22/9473/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/22/9473/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mayer Alvo & Jingrui Mu, 2023. "COVID-19 Data Analysis Using Bayesian Models and Nonparametric Geostatistical Models," Mathematics, MDPI, vol. 11(6), pages 1-13, March.
    2. Luca Grassetti & Laura Rizzi, 2019. "The determinants of individual health care expenditures in the Italian region of Friuli Venezia Giulia: evidence from a hierarchical spatial model estimation," Empirical Economics, Springer, vol. 56(3), pages 987-1009, March.
    3. Ferreira, Marco A.R. & Porter, Erica M. & Franck, Christopher T., 2021. "Fast and scalable computations for Gaussian hierarchical models with intrinsic conditional autoregressive spatial random effects," Computational Statistics & Data Analysis, Elsevier, vol. 162(C).
    4. John M. Humphreys, 2022. "Amplification in Time and Dilution in Space: Partitioning Spatiotemporal Processes to Assess the Role of Avian-Host Phylodiversity in Shaping Eastern Equine Encephalitis Virus Distribution," Geographies, MDPI, vol. 2(3), pages 1-16, July.
    5. Yuheng Ling, 2020. "Time, space and hedonic prediction accuracy: evidence from Corsican apartment markets," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 64(2), pages 367-388, April.
    6. Chiranjit Dutta & Nalini Ravishanker & Sumanta Basu, 2022. "Modeling Multivariate Positive-Valued Time Series Using R-INLA," Papers 2206.05374, arXiv.org, revised Jul 2022.
    7. Ropo E. Ogunsakin & Themba G. Ginindza, 2022. "Bayesian Spatial Modeling of Diabetes and Hypertension: Results from the South Africa General Household Survey," IJERPH, MDPI, vol. 19(15), pages 1-17, July.
    8. Carson, Stuart & Mills Flemming, Joanna, 2014. "Seal encounters at sea: A contemporary spatial approach using R-INLA," Ecological Modelling, Elsevier, vol. 291(C), pages 175-181.
    9. V. A. Alegana & C. Pezzulo & A. J. Tatem & B. Omar & A. Christensen, 2021. "Mapping out-of-school adolescents and youths in low- and middle-income countries," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-10, December.
    10. Nikoline N. Knudsen & Jörg Schullehner & Birgitte Hansen & Lisbeth F. Jørgensen & Søren M. Kristiansen & Denitza D. Voutchkova & Thomas A. Gerds & Per K. Andersen & Kristine Bihrmann & Morten Grønbæk , 2017. "Lithium in Drinking Water and Incidence of Suicide: A Nationwide Individual-Level Cohort Study with 22 Years of Follow-Up," IJERPH, MDPI, vol. 14(6), pages 1-13, June.
    11. Scott, Ryan P. & Scott, Tyler A., 2019. "Investing in collaboration for safety: Assessing grants to states for oil and gas distribution pipeline safety program enhancement," Energy Policy, Elsevier, vol. 124(C), pages 332-345.
    12. Cho, Daegon & Hwang, Youngdeok & Park, Jongwon, 2018. "More buzz, more vibes: Impact of social media on concert distribution," Journal of Economic Behavior & Organization, Elsevier, vol. 156(C), pages 103-113.
    13. Brown, Paul T. & Joshi, Chaitanya & Joe, Stephen & Rue, Håvard, 2021. "A novel method of marginalisation using low discrepancy sequences for integrated nested Laplace approximations," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    14. Massimo Bilancia & Giacomo Demarinis, 2014. "Bayesian scanning of spatial disease rates with integrated nested Laplace approximation (INLA)," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(1), pages 71-94, March.
    15. Douglas R. M. Azevedo & Marcos O. Prates & Dipankar Bandyopadhyay, 2021. "MSPOCK: Alleviating Spatial Confounding in Multivariate Disease Mapping Models," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(3), pages 464-491, September.
    16. Braulio-Gonzalo, Marta & Bovea, María D. & Jorge-Ortiz, Andrea & Juan, Pablo, 2021. "Which is the best-fit response variable for modelling the energy consumption of households? An analysis based on survey data," Energy, Elsevier, vol. 231(C).
    17. Isabel Martínez-Pérez & Verónica González-Iglesias & Valentín Rodríguez Suárez & Ana Fernández-Somoano, 2021. "Spatial Distribution of Hospitalizations for Ischemic Heart Diseases in the Central Region of Asturias, Spain," IJERPH, MDPI, vol. 18(23), pages 1-10, November.
    18. Maike Tahden & Juliane Manitz & Klaus Baumgardt & Gerhard Fell & Thomas Kneib & Guido Hegasy, 2016. "Epidemiological and Ecological Characterization of the EHEC O104:H4 Outbreak in Hamburg, Germany, 2011," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-19, October.
    19. Zhang, Shen & Liu, Xin & Tang, Jinjun & Cheng, Shaowu & Qi, Yong & Wang, Yinhai, 2018. "Spatio-temporal modeling of destination choice behavior through the Bayesian hierarchical approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 537-551.
    20. Shuangshuang Xu & Marco A. R. Ferreira & Erica M. Porter & Christopher T. Franck, 2023. "Bayesian model selection for generalized linear mixed models," Biometrics, The International Biometric Society, vol. 79(4), pages 3266-3278, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:22:p:9473-:d:444962. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.