Differences in basic digital competences between male and female university students of Social Sciences in Spain

  1. Esteban Vázquez-Cano 1
  2. Eloy López Meneses 2
  3. Eduardo García-Garzón 3
  1. 1 Universidad Nacional de Educación a Distancia
    info

    Universidad Nacional de Educación a Distancia

    Madrid, España

    ROR https://ror.org/02msb5n36

  2. 2 Universidad Pablo de Olavide
    info

    Universidad Pablo de Olavide

    Sevilla, España

    ROR https://ror.org/02z749649

  3. 3 Universidad Autónoma de Madrid
    info

    Universidad Autónoma de Madrid

    Madrid, España

    ROR https://ror.org/01cby8j38

Mostrar afiliaciones +
Revista:
International Journal of Educational Technology in Higher Education

ISSN: 2365-9440

Año de publicación: 2017

Número: 14

Tipo: Artículo

DOI: 10.1186/S41239-017-0065-Y DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: International Journal of Educational Technology in Higher Education

Resumen

This article analyses the differences in basic digital competences of male and female university students on Social Education, Social Work and Pedagogy courses. The study of gender differences in university students’ acquisition of digital competence has considerable didactic and strategic consequences for the development of these skills. The study was carried out at two public universities in Spain (UNED – the National Distance-Learning University, and the Universidad Pablo de Olavide) on a sample of 923 students, who responded to a questionnaire entitled “University Students’ Basic Digital Competences 2.0” (COBADI – registered at the Spanish Patent and Trademark Office). The research applied a quantitative methodology based on a Bayesian approach using multinomial joint distribution as prior distribution. The use of Bayes factors also offers advantages with respect to the use of frequentist p-values, like the generation of information on the alternative hypothesis, that the evidence is not dependent on the sample size used. The results show that men have greater perceived competence in digital cartography and online presentations, whereas women prefer to request personal tutorials to resolve doubts about technology and have greater perceived competence in corporate emailing. There is also evidence that the men have greater perceived competence in developing “online presentations” than women do. Regarding to, “Interpersonal competences in the use of ICT at university”, we observed that the female students opted for personal sessions with tutors in greater numbers than the male students did.

Referencias bibliográficas

  • Aguaded I., & Cabero J. (2013). Tecnologías y medios para la educación en la e-sociedad (). Madrid: Alianza Editorial.
  • American Statistical Association (2016). Guidelines for Assessment and Instruction in Statistics Education College Report 2016 (). GAISE College Report ASA Revision Committee Retrieved from http://www.amstat.org/education/gaise.
  • Area M. (2014). Alfabetización digital y competencias profesionales para la información y la comunicación. Organización y gestión educativa: Revista del Fórum Europeo de Administradores de la Educación, 22(1), 9–13.
  • Ares V. M. (1999). La prueba de significación de la “hipótesis cero” en las investigaciones por encuesta. Metodología de Encuestas, 1, 47–68.
  • Berry D. A. (1995). Basic statistics: A Bayesian perspective (). Belmont: Wadsworth.
  • Díaz C., & de la Fuente I. (2004). Controversias en el uso de la inferencia en la investigación experimental. Metodología de las Ciencias del Comportamiento, Volumen especial, 161–167.
  • Díaz C., & Batanero C. (2006). ¿Cómo puede el méto do bayesiano contribuir a la investigación en psicología y educación? Paradígma, 27(2), 35–53.
  • Dienes Z. (2011). Bayesian versus orthodox statistics: Which side are you on? Perspectives on Psychological Science, 6(3), 274–290.
  • Edwards W., Lindman H., & Savage L. J. (1963). Bayesian statistical inference for psychological research. Pshychological Review, 70, 193–242.
  • Egbo O. P., Chinwe R., Ikechukwu C., & Onwumere J. U. (2011). Gender perception and attitude towards ELearning: A case of Business students, University of Nigeria. International Journal of Computer Application, 1, 135–148.
  • European Commission (2008). Mapping the maze: Getting more women to the top in research (). European Communities: Science, Economy and Society. Scientific Culture, and Gender Issues.
  • Falk R., & Greenbaum C. W. (1995). Significance tests die hard: The amazing persistence of a probabilistic misconception. Theory and Psychology, 5(1), 75–98.
  • Fernández-Márquez E., Vázquez-Cano E., & López Meneses E. (2016). Los mapas conceptuales multimedia en la educación universitaria: recursos para el aprendizaje significativo. Campus Virtuales, 5(1), 10–18.
  • Finch S., Cumming G., & Thomason N. (2001). Reporting of statistical inference in the Journal of applied psychology: Little evidence of reform. Educational and Psychological Measurement, 61, 181–210.
  • Garbarino E., & Strahilevitz M. (2004). Gender differences in the perceived risk of buying online and the effects of receiving a site recommendation. Journal of Business Research, 57, 768–775.
  • George D., & Mallery P. (2003). SPSS for windows step by step: A simple guide and reference. 11.0 update (4.ª ed., ). Boston: Allyn & Bacon.
  • Goswami A., & Dutta S. (2016). Gender differences in technology usage. A literature Review. Open Journal of Business and Management, 4, 51–59 http://dx.doi.org/10.4236/ojbm.2016.41006.
  • Hager W. (2000). About some misconceptions and the discontent with statistical tests in psychology. Methods on Psychological Research, 5, 1 Retrieved from http://hbanaszak.mjr.uw.edu.pl/TempTxt/Hager_2000_About%20some %20misconceptions%20and%20the%20discontent%20with%20statistical%20tests%20in%20psychology.
  • Hupfer, M. E., & Detlor, B. (2006). Gender and web information seeking: A self-concept orientation model. Journal of the Association for Information Science and Technology, 57, 105-115. doi: 10.1002/asi.20379
  • Islam A., Abdul Rahim N., Chee Liang A. T., & Momtaz H. (2011). Effect of demographic factors on E-learning effectiveness in a higher learning institution in Malaysia. International Education Studies, 4, 112–122 http:// dx.doi.org/10.5539/ies.v4n1p112.
  • Jackson L. A., Ervin K. S., Gardner P. D., & Ervin N. S. (2001). Gender and the Internet: Women communicating and men searching. Sex Roles: A Journal of Research, 44, 363–379 http://dx.doi.org/10.1023/A:1010937901821.
  • Jamil T., Ly A., Morey R. D., Love J., Marsman M., & Wagenmakers E.-J. (2015). Default "Gunel and Dickey" Bayes factors for contingency tables. Behaviour Research Methods, 48, 1–16.
  • Jarosz A. F., & Wiley J. (2014). What are the odds? A practical guide to computing and reporting Bayes factors. The Journal of Problem Solving, 7, 1 Article. 2. http://dx.doi.org/10.7771/1932-6246.1167.
  • Lecoutre B. (1996). Traitement statistique des données expérimentales: Des pratiques traditionnelles aux pratiques bayésiennes. Paris: CISIA.
  • Li N., & Kirkup G. (2007). Gender and cultural differences in Internet use: A study of China and the UK. Computers and Education, 48, 301–317 http://dx.doi.org/10.1016/j.compedu.2005.01.007.
  • Liaw S., & Huang H. (2011). A study of investigating learners’ attitudes toward E-learning. In Proceedings of the 5th International Conference on Distance Learning and Education (pp. 28–32).
  • Lindley D. V. (1993). The analysis of experimental data: The appretiation of tea and wine. Teaching Statistics, 15(1), 22–25.
  • Liu T., & Sun H. (2012). Gender differences on information literacy of science and engineering undergraduates. International Journal of Modern Education and Computer Science, 4(2), 23–30.
  • Liu Z., & Huang X. (2008). Gender differences in the online reading environment. Journal of Documentation, 64(4), 616–626.
  • Lubke G. H., & Muthen B. (2004). Applying Multigroup confirmatory factor models for continuous outcomes to Likert scale data complicates meaningful group comparisons. Structural Equation Modeling, 11, 514–534.
  • Mazman S., & Yasemin K. (2011). Gender differences in using social networks. TOJET: The Turkish Online Journal of Educational Technology, 10(2), 133–139.
  • Morey, R.D., & Rouder, J.N. (2015). Bayes factor: Computation of Bayes factors for common designs. R package version 0. 9.12–2. https://CRAN.R-project.org/package=BayesFactor
  • Nysveen H., Pedersen P., & Thorbjørnsen H. (2005). Explaining intention to use mobile chat services: Moderating effects of gender. Journal of Consumer Marketing, 22, 247–256 http://dx.doi.org/10.1108/07363760510611671.
  • O’Hagan A., & Forster J. (2004). Bayesian inference. Kendall’s advanced Theory of statistics (). London: Arnold.
  • OCDE (2010a). Working paper 21st century skills and competences for new millennium learners in OECD countries.(EDU Working paper no. 41).
  • OCDE (2010b). PISA 2009 at a glance (). OECD Publishing http://dx.doi.org/10.1787/9789264095298-en.
  • Okazaki S., & Renda dos Santos L. M. (2012). Understanding E-learning adoption in Brazil: Major determinants and gender effects. International Review of Research in Open and Distributed Learning, 13, 91–106.
  • Ong C., & Lai J. Y. (2006). Gender differences in perceptions and relationships among dominants of E-learning acceptance. Computers in Human Behaviour, 22, 816–826 http://dx.doi.org/10.1016/j.chb.2004.03.006.
  • Pruzek R. M. (1997). An introduction to bayesian inference and its applications. In L. L. Harlow, S. A. Mulaik, & J. H. Steiger (Eds.), What if there were no significance tests? (pp. 287–318). Mahwah: Lawrence Erlbaum.
  • Rajagopal I., & Bojin N. (2003). A gendered world: Students and instructional technologies. First Monday, 8, 1 Retrieved from http://firstmonday.org/article/viewArticle/1023/944.
  • Raman A., Rozalina Khalid Y., & Rizuan M. (2014). Usage of learning management system (Moodle) among postgraduate students: UTAUT model. Asian Social Science, 10, 186–195 http://dx.doi.org/10.5539/ass.v10n14p186.
  • Richards G., Magee C., & Artino A. R. (2012). You can't fix by analysis what you've spoiled by design: Developing survey instruments and collecting validity evidence. Journal of Graduate Medical Education, 4(4), 407–410.
  • Rindskopf, D. M. (1997). Classical and bayesian approaches. En L. L. Harlow, S. A. Mulaik, & J. H. Steiger. What if there were no significance tests? (319-334). Mahwah: Lawrence Erlbaum Associates.
  • Rouder J. N., & Morey R. D. (2012). Default Bayes factors for model selection in regression. Multivariate Behavioral Research, 47, 877–903.
  • Rouder J. N., Speckman P. L., Sun D., Morey R. D., & Iverson G. (2009). Bayesian t-tests for accepting and rejecting the null hypothesis. Psychonomic Bulletin & Review, 16, 225–237.
  • Rozeboom, W. W. (1970). The fallacy of the null hypothesis significance test. In. D. E. Morrison, & R. E. Henkel, The significance tests controversy: A reader (216-230). Chicago: Aldine.
  • Sevillano M. A. L., & Vázquez-Cano E. (2015). The impact of digital mobile devices in higher education. Educational Technology & Society, 18(1), 106–118.
  • Suri G., & Sharma S. (2013). The impact of gender on attitude towards computer technology and E-learning: An exploratory study of Punjab university, India. International Journal of Engineering Research, 2, 132–136.
  • Tahira Jamil A., Morey R. D., Love J., Marsman M., & Wagenmakers E. (2017). Default “Gunel and dickey” Bayes factors for contingency tables. Behavior Research Methods, 49(2), 638–652.
  • Tüfekçi Z. (2008). Gender, social capital and social network(ing) sites: Women bonding, men searching (). Boston: Annual meeting of the American Sociological Association. Sheraton Boston and the Boston Marriott Copley Place.
  • United Nations (2014). Measuring ICT and gender: An assessment (). New York and Geneva: United Nations Retrieved from http://unctad.org/en/PublicationsLibrary/webdtlstict2014d1_en.pdf.
  • Vázquez-Cano E. (2012). Mobile learning with twitter to improve linguistic competence at secondary schools. The New Educational Review, 29(3), 134–147.
  • Vázquez-Cano, E. (2014). Mobile distance learning with Smartphones and apps in higher education. Educational Sciences: Theory & Practice, 14(4), 1-16. DOI: 10.12738/est.2014.4.2012
  • Vázquez-Cano E., López Meneses E., & Sáez López J. M. (2016). La imagen de los países a través de una didáctica digital ubicua. Un estudio de caso en México. Revista Mexicana de Investigación Educativa, 21(68), 17–44.
  • Vázquez-Cano E., Mengual-Andrés S., & Roig-Vila R. (2015). Análisis lexicométrico de la especificidad de la escritura digital del adolescente en Whastapp. Revista de Lingüística Teórica y Aplicada, 53(1), 83–105.
  • Venkatesh V., & Morris M. G. (2000). Why Don’t men ever stop to ask for directions? MIS Quarterly, 24, 115–139 http://dx. doi.org/10.2307/3250981.
  • Venkatesh V., Morris M. G., Davis G., & Davis F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27, 425–478.
  • Wagenmakers E. (2007). A practical solution to the pervasive problems of p values. Psychonomic Bulletin and Review, 14(5), 779–804 http://dx.doi.org/10.3758/BF03194105.
  • Wagenmakers E., Beek T. F., Rotteveel M., Gierholz A., Matzke D., Steingroever H., … Pinto Y. (2015). Turning the hands of time again: A purely confirmatory replication study and a Bayesian analysis. Frontiers in Psychology, 6, 494 http:// dx.doi.org/10.3389/fpsyg.2015.00494.
  • Western B. (1999). Bayesian analysis for sociologists: An introduction. Sociological Methods & Research, 28(1), 7–34.
  • Wetzels R., Matzke D., Lee M. D., Rouder J. N., Iverson G. J., & Wagenmakers E. J. (2011). Statistical evidence inexperimental psychology: An empirical comparison using 855 t tests. Perspectives on Psychological Science, 6(3), 291–298 http://dx.doi.org/10.1177/1745691611406923.
Fuente de los datos: Dialnet