Ejemplificación de metodología mixta para el análisis del uso de entornos blended learning en docentes universitarios

  1. Antonio Víctor Martín García 1
  2. María Cruz Sánchez Gómez 1
  3. Bárbara Gutiérrez Pérez 1
  1. 1 Universidad de Salamanca
    info

    Universidad de Salamanca

    Salamanca, España

    ROR https://ror.org/02f40zc51

Revista:
RISTI: Revista Ibérica de Sistemas e Tecnologias de Informação

ISSN: 1646-9895

Año de publicación: 2019

Número: 33

Páginas: 16-31

Tipo: Artículo

DOI: 10.17013/RISTI.33.16-31 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: RISTI: Revista Ibérica de Sistemas e Tecnologias de Informação

Resumen

El objetivo de este trabajo es averiguar a partir de las experiencias y percepciones de profesores universitarios, las principales expectativas, actitudes, demandas, ventajas y desventajas de la aceptación -y adopción- de las metodologías de Blended Learning (BL) en la enseñanza superior. El trabajo empírico se realizó siguiendo un diseño metodológico mixto de tipo derivativo secuencial de equivalencia de status. La información cuantitativa fue recogida con un cuestionario, elaborado ad hoc, al que contestaron 980 profesores universitarios; de éstos, 86 manifestaron de forma abierta su opinión sobre estas metodologías. Para integrar los resultados de los análisis CUANT-CUAL se realizó un análisis DAFO en el que se ponen de manifiesto, a juicio de los docentes participantes en el estudio, las debilidades, amenazas, fortalezas y oportunidades de la enseñanza B-learning, a nivel interno y externo de las instituciones universitarias

Referencias bibliográficas

  • Al-Azawei, A., Parslow, P., & Lundqvist, K. (2017). Investigating the effect of learning styles in a blended e-learning system: An extension of the technology acceptance model (TAM). Australasian Journal of Educational Technology, 33(2).
  • Al-Busaidi, K. A. (2013). An empirical investigation linking learners’ adoption of blended learning to their intention of full e-learning. Behaviour & Information Technology, 32(11), 1168–1176. DOI: https ://doi.org/10.1080/01449 29X.2013.774047
  • Aldunate, R., & Nussbaum, M. (2013). Teacher adoption of technology. Computers in Human Behavior, 29(3), 519–524. DOI: https://doi.org/10.1016/j.chb.2012.10.017
  • Aliyu, O., Arasanmi, C. C., & Ekundayo, S. (2019). Do demographic characteristics moderate the acceptance and use of the Moodle learning system among business students?. International Journal of Education and Development using ICT, 15(1).
  • Barrera-Barrera, R., Rey-Moreno, M., & Medina-Molina, C. (2019). Fatores explicativosda preferência e uso da administração eletrônica na Espanha. Revista deAdministração Pública, 53(2), 349–374. DOI: http://dx.doi.org/10.1590/0034-761220170391
  • Bliuc, A. M., Casey, G., Bachfischer, A., Goodyear, P., & Ellis, R. A. (2012). Blended learning in vocational education: teachers’ conceptions of blended learning and their approaches to teaching and design. The Australian Educational Researcher, 39(2), 237–257.
  • Boelens, R., Voet, M., & De Wewer, B. (2018). The design of blended learning in response to student diversity in higher education: Instructors’ views and use of differentiated instruction in blended learning. Computers & Education, 120, 197–212. DOI: https://doi.org/10.1016/j.compedu.2018.02.009
  • Cavanaugh, C., Hargis, J., & Mayberry, J. (2016). Participation in the Virtual Environment of Blended College Courses: An Activity Study of Student Performance. International Review of Research in Open and Distributed Learning, 17(3), 263–275. doi: http://dx.doi.org/10.19173/irrodl.v17i3.1811
  • Chen, J. L. (2011). The effects of education compatibility and technological expectancy on e-learning acceptance. Computers & Education, 57, 1501–1511.
  • Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management science, 35(8), 982–1003.
  • Dziuban, C., Graham, C. R., Moskal, P. D., Norberg, A., & Sicilia, N. (2018). Blended learning: The new normal and emerging technologies. International Journal of Educational Technology in Higher Education, 15, 1–16. doi: http://dx.doi.org/10.1186M1239-017-0087-5
  • Ginns, P., & Ellis, R.A. (2009). Evaluating the quality of e-learning at the degree level in the student experience of blended learning. British Journal of Educational Technology, 40(4), 652–663.
  • Graham, C. R., Woodfield, W., & Harrison, J. B. (2013). A framework for institutional adoption and implementation of blended learning in higher education. The internet and higher education, 18, 4–14.
  • Ilic, D., Hart, W., Fiddes, P., Misso, M., & Villanueva, E. (2013). Adopting a blended learning approach to teaching evidence based medicine: a mixed methods study. BMC medical education, 13(1), 169.
  • Ilic, D., Nordin, R. B., Glasziou, P., Tilson, J. K., & Villanueva, E. (2013). Implementation of a blended learning approach to teaching evidence based practice: a protocol for a mixed methods study. BMC medical education, 13(1), 170. Imtiaz, M. A., & Maarop, N. (2014). A review of technology acceptance studies in the field of education. Jurnal Teknologi, 69(2).
  • Jornet Meliá, J., González-Such, J., & García-Bellido, A. (2012). La Investigación Evaluativa y las Tecnologías de la Información y la Comunicación (TIC). Revista Española de Pedagogía, 251, 93–110.
  • Kim, H. W., Kankanhalli, A., & Lee, H. L. (2016). Investigating decision factors in mobile application purchase: A mixed-methods approach. Information & Management, 53(6), 727–739.
  • Konak, A., Kulturel-Konak, S., Nasereddin, M., & Bartolacci, M. R. (2017). Impact of Collaborative Work on Technology Acceptance: A Case Study from Virtual Computing. Journal of Information Technology Education, 16(1), 15–19. Doi: https://doi.org/10.28945/3622
  • Lai, M., Lam, K. M., & Lim, C. P. (2016). Design principles for the blend in blended learning: a collective case study. Teaching in Higher Education, 21(6), 716–729. Doi: doi.org/10.1080/13562517.2016.1183611
  • Lee, B. C., Yoon, J. O., & Lee, I. (2009). Learners’ acceptance of e-learning in South Korea: Theories and results. Computers & Education, 53(4), 1320–1329.
  • Lee, Y., Kozar, K. A., & Larsen, K. R. (2003). The technology acceptance model: Past, present, and future. Communications of the Association for information systems, 12(1), 50. DOI: 10.17705/1CAIS.01250
  • Lim, D. H., & Morris, M. L. (2009). Learner and Instructional Factors Influencing Learning Outcomes within a Blended Learning Environment. Educational Technology & Society, 12 (4), 282–293.
  • Marangunić, N., & Granić, A. (2015). Technology acceptance model: a literature review from 1986 to 2013. Universal Access in the Information Society, 14(1), 81–95.
  • Martín-García, A.V., & Sánchez-Gómez, M.C. (2014). Modelo predictivo de la intención de adopción de Blended learning en profesores universitarios. Universitas Psychologica, 13(2), 601–614.
  • Martín-García, A.V., Martínez Abad, F., & Reyes González, D. (2019). TAM and stages of adoption of blended learning in higher education by application of data mining techniques. British Journal of Educational Technology, 50, 2484–2500. Doi: https://doi.org/10.1111/bjet.12831
  • Martín-García, A.V., Sánchez-Gómez, M. C., & Costa, P. (2019) Percepción de Blended Learning en profesores universitarios de distintos ámbitos disciplinares. Revista Lusófona de Educação, 44, 117–133. Doi: 10.24140/issn.1645-7250.rle44.0
  • Ngai, E. T., Poon, J. L., & Chan, Y. C. (2007). Empirical examination of the adoption of WebCT using TAM. Computers & Education, 48(2), 250–267. Doi: 10.1016/j. compedu.2004.11.007
  • Picciano, A. G., & Seaman, J. (2007). K-12 online learning: a survey of US school district administrators. Boston: Sloan Consortium. Retrieved from: https://bit.ly/2TPb1o3
  • Poon, J. (2013). Blended learning: An institutional approach for enhancing students’ learning experiences. Journal of online learning and teaching, 9(2), 271–288.
  • Pynoo, B., Tondeur, J., Van Braak, J., Duyck, W., Sijnave, B., & Duyck, P. (2012). Teachers’ Acceptance and Use of an Educational Portal. Computers & Education, 58(4), 1308–1317. Doi: https://doi.org/10.1016/j.compedu.2011.12.026
  • Pynoo, P., Devolder, J., Tondeur, J., Van Braak, W., & Duyck, P. (2011). Predicting secondary school teachers’ acceptance and use of a digital learning environment: A cross-sectional study. Computers in Human Behavior, 27 (1), 568–575. Doi: 10.1016/j.chb.2010.10.005
  • Scherer, R., Siddiq, F., & Teo, T. (2015). Becoming more specific: Measuring andmodeling teachers’ perceived usefulness of ICT in the context of teaching andlearning. Computers & Education, 88, 202-214. Doi: https://doi.org/10.1016/j.compedu.2015.05.005
  • Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, 128, 13–35. Doi: https ://doi.org/10.1016/j.compe du.2018.09.009
  • Selim, H. M. (2007). Critical Success Factors for E-Learning Acceptance: Confirmatory Factor Models. Computers & Education, 49(2), 396–413.
  • Šumak, B., & Šorgo, A. (2016). The acceptance and use of interactive whiteboards amongteachers: Differences in UTAUT determinants between pre- and post-adopters.Computers in Human Behavior, 64, 602-620. Doi: https://doi.org/10.1016/j.chb.2016.07.037
  • Teo, T. (2011). Factors Influencing Teachers’ Intention to Use Technology: Model Development and Test. Computers & Education, 57(4), 2432–2440.
  • Teo, T., Fan, X., & Du, J. (2015). Technology acceptance among pre-service teachers: Does gender matter?. Australasian Journal of Educational Technology, 31, 235– 251. DOI: https://doi.org/10.14742/ajet.1672 https://bit.ly/2Cm8vi7
  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. Doi: https://bit.ly/2kFEjEE
  • Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315.
  • Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186–204.
  • Venkatesh, V., Brown, S. A., Maruping, L. M., & Bala, H. (2008). Predicting different conceptualizations of system use: the competing roles of behavioral intention, facilitating conditions, and behavioral expectation. MIS quarterly, 32(3), 483–5.
  • Wu, P. F. (2011). A mixed methods approach to technology acceptance research. Journal of the AIS, 13(3), 172–187.