Teacher competences in the use of STEM virtual simulations: design and validation of a measurement instrument (CDUSV)
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INTRODUCTION. The importance of using virtual simulations as a didactic resource to develop a contextualized applied science teaching, focused on the development of scientific competencies, has created the need to assess the competencies of Secondary Education teachers in the use of these tools. The aim of this work is to design and validate an instrument to assess the competencies of secondary education teachers in STEM areas, in the use of virtual simulations. METHOD. Based on an exhaustive theoretical review, an initial instrument was developed, which was subjected to a validation process by experts. Then, a pilot study with 30 teachers was carried out, which helped to build the definitive instrument. The final instrument was applied to 332 Secondary Education teachers of STEM areas in Spain. Finally, the validation of the instrument was carried out through an exploratory factor analysis (EFA). RESULTS. The results obtained show a high content validity and reliability (Cronbach’s α = .92). Furthermore, the exploratory factor analysis developed using the Maximum Likelihood Estimation method and Equamax rotation shows a structure of 4 factors called: “Development of teaching practice”, “Didactic planning”, “Self-reflection on teaching practice” and “Difficulties associated with the use of virtual simulations”. A high reliability is obtained for all the extracted factors. DISCUSSION. Finally, it is discussed the value of the instrument and its interest for the design of teacher training programs built from the knowledge and the real difficulties of teachers in the use of virtual STEM simulations in the Secondary Education classroom.
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References
Alkhaldi, T., Pranata, I. y Athauda, R. I. (2016). A review of contemporary virtual and remote laboratory implementations: observations and findings. Journal of Computers in Education, 3(3), 329-351. https://doi.org/10.1007/s40692-016-0068-z
Asparouhov, T. y Muthen, B. (2009). Exploratory structural equation modeling. Structural Equation Modeling, 16, 397-438.
Bandalos, D. L. y Finney, S. J. (2010). Factor analysis: exploratory and confirmatory. En G. R. Hancock y R. O. Mueller (eds.), Reviewer’s guide to quantitative methods. Routledge.
Blake, C. y Scanlon, E. (2007). Reconsidering simulations in science education at a distance: features of effective use. Journal of Computer Assisted Learning, 23, 491-502. https://doi.org/10.1111/j.1365-2729.2007.00239.x
Cabero, J., Marín, V. y Castaño, C. (2015). Validación de la aplicación del modelo TPACK para la formación del profesorado en TIC. @tic. Revista d’Innovació Educativa, 14, 13-22. https://doi.org/10.7203/attic.14.4001
Cabero, J., Pérez, J. L. y Llorente, C. (2018). Estructural equation model and validation of the TPACK model: empirical study [Modelo de ecuaciones estructurales y validación del modelo de formación TPACK: estudio empírico]. Profesorado, 22(4), 353-376. https://doi.org/10.30827/profesorado.v22i4.8420
Carlos-Guzmán, J. (2016). ¿Qué y cómo evaluar el desempeño docente? Una propuesta basada en los factores que favorecen el aprendizaje. Propósitos y Representaciones, 4(2), 285-358. http://dx.doi.org/10.20511/pyr2016.v4n2.124
Cebrián-Cienfuentes, S., Ros, C., Fernández-Piqueras, R. y Guerrero, E. (2021). Análisis de la competencia digital docente y uso de recursos TIC tras un proceso de intervención universitario, basado en la implementación de una metodología innovadora de gamificación. Bordón, Revista de Pedagogía, 73(2), 41-61. https://doi.org/10.13042/Bordon.2021.87134
Chan, P., Van Gerven, T., Dubois, J. y Bernaerts, K. (2021). Virtual chemical laboratories: a systematic literature review of research, technologies and instructional design. Computers and Education Open, 2. https://doi.org/10.1016/j.caeo.2021.100053
Costello, A. B. y Osborne, J. (2005). Best practices in exploratory factor analysis: four recommendations for getting the most from your analysis. Practical Assessment Research & Evaluation, 10(7), 1-9. https://doi.org/10.7275/jyj1-4868
D’Angelo, C., Rutstein, D., Harris, C., Bernard, R., Borokhovski, E. y Haertel, G. (2014). Simulations for STEM learning: systematic review and meta-analysis. SRI Education, 58.
De Jong, T. y Van Joolingen, W. R. (1998). Scientific discovery learning with computer simulations of conceptual domains. Review of Educational Research, 68(2), 179-201. https://doi.org/10.3102/00346543068002179
Escobar, I. M. C. y Adames, C. P. (2008). Competencias docentes de profesores de pregrado: diseño y validación de un instrumento de evaluación. Universitas Psychologica, 7(2), 456-456. https://revistas.javeriana.edu.co/index.php/revPsycho/article/view/441
Fernández, M. J., Rodríguez, J. M. y Fernández, F. J. (2016). Evaluación de competencias docentes del profesorado para la detección de necesidades formativas. Bordón, Revista de Pedagogía, 68(2), 85-101. https://doi.org/10.13042/Bordon.2016.68206
Ferrando, P. J. y Anguiano-Carrasco, C. (2010). El análisis factorial como técnica de investigación en psicología. Papeles del Psicólogo, 31(1), 18-33.
Ferrari, A. (2013). DIGCOMP: a framework for developing and understanding digital competence in Europe. European Commission. Joint Research Centre. Institute for Prospective Technological Studies.
Flora, D. B., LaBrish, C. y Chalmers, R. P. (2012). Old and new ideas for data screening and assumption testing for exploratory and confirmatory factor analysis. Frontiers in Quantitative Psychology and Measurement, 3(55), 1-21. https://doi.org/10.3389/fpsyg.2012.00055
Forero, C. G., Maydeu-Olivares, A. y Gallardo-Pujol, D. (2009). Factor analysis with ordinal indicators: a Monte Carlo study comparing DWLS and ULS estimation. Structural Equation Modeling, 16, 625-641. https://doi.org/10.1080/10705510903203573
Gable, R. K. y Wolf, J. W. (1993). Instrument development in the affective domain: measuring attitudes and values in corporate and school settings. Kluwer Academic.
Geelan, D. y Fan, X. (2013). Enhancing students’ scientific literacy in science education using interactive simulations: a critical literature review. Journal of Computers in Mathematics and Science Teaching, 32, 125-171.
Gisbert, M., González, J. y Esteve, F. M. (2016). Competencia digital y competencia digital docente: una panorámica sobre el estado de la cuestión. Revista Interuniversitaria de Investigación en Tecnología Educativa, 0, 74-83. https://doi.org/10.6018/riite2016/257631
Gnesdilow, D. y Puntambekar, S. (2021). Comparing middle school students’ science explanations during physical and virtual laboratories. Journal of Science Education and Technology, 31(4), 191-202. https://doi.org/10.1007/s10956-021-09941-0
González, J. y Wagenaar, R. (2008). La contribución de las universidades al proceso de Bolonia. Una introducción a Tuning Educational Structures in Europe (edición española). Publicaciones de la Universidad de Deusto.
Grant, J. S. y Davis, L. L. (1997). Selection and use of content experts for instrument development. Research in Nursing & Health, 20, 269-274.
Hall, R., Atkins, L. y Fraser, J. (2014). Defining a self-evaluation digital literacy for secondary educators. The DigiLit Leicester Project. Research in Learning Technology, 22. https://doi.org/10.3402/rlt.v22.21440
Harada, A. S. (2021). La comunidad docente y las competencias digitales: la formación a lo largo de la vida. Revista Conhecimento Online, 1, 177-192. https://doi.org/10.25112/rco.v1i0.2400
Hutkemri, E. Z. (2014). Impact of using GeoGebra on students’ conceptual and procedural knowledge of limit function. Mediterranean Journal of Social Sciences, 5(23), 873-881. https://doi.org/10.5901/mjss.2014.v5n23p873
INTEF (2017). Marco de Competencia Digital. Ministerio de Educación, Ciencia y Deportes.
Jimoyiannis, A. (2010). Developing a technological pedagogical content knowledge framework for science education: implications of a teacher trainers’ preparation program. Proceedings of Informing Science & IT Education Conference (InSITE), 597-607.
Kelley, T. L. (1935). Essential traits of mental life. Harvard Studies in Education, vol. 26. Harvard University Press.
Koehler, M. J. y Mishra, P. (2008). Introducing technological pedagogical content knowledge. En A. E. R. Association (ed.), Annual Meeting of the American Educational Research Association.
Lee, Y. H., Waxman, H., Wu, J. Y., Michko, G. y Lin, G. (2013) Revisit the effect of teaching and learning with technology. Educ Technol Soc, 16(1), 133-146.
Lévy-Mangin, J. P., Varela-Mallou, J. y Abad-González, J. (2006). Modelización con estructuras de covarianzas en ciencias sociales: temas esenciales, avanzados y aportaciones especiales. Netbiblo.
Lloret-Segura, S., Ferreres-Traver, A., Hernández-Baeza, A. y Tomás-Marco, I. (2014). El análisis factorial exploratorio de los ítems: una guía práctica, revisada y actualizada. Anales de Psicología, 30(3), 1151-1169. http://dx.doi.org/10.6018/analesps.30.3.199361
Lorenzo-Seva, U., Timmerman, M. E. y Kiers, H. A. L. (2011). The Hull method for selecting the number of common factors. Multivariate Behavioral Research, 46(2), 340-364. http://doi.org/10.1080/00273171.2011.564527
Lorenzo-Seva, U. y Ferrando, P. J. (2020). FACTOR (n.o de versión 10.10.03). Windows. Tarragona (Spain): Rovira i Virgili University. http://psico.fcep.urv.es/utilitats/factor
Menchaca, I., Dziabenko, O. y García, J. (2020). Experiencia española en el proyecto Go-Lab. Educar, 56(2), 387-405. https://doi.org/10.5565/rev/educar.1067
O’Dwyer, L. y Bernauer, J. (2014). Quantitative research for the qualitative researcher. SAGE Publications, Inc. https://doi.org/10.4135/9781506335674
Pelgrum, W. J. (2001). Obstacles to the integration of ICT in education: results from a worldwide educational assessment. Computers & Education, 37(2), 163-178. https://doi.org/10.1016/S0360-1315(01)00045-8
Perales-Palacios, F. J. y Aguilera, D. (2020). Ciencia-tecnología-sociedad vs. STEM: ¿evolución, revolución o disyunción? Ápice. Revista de Educación Científica, 4(1), 1-15. https://doi.org/10.17979/arec.2020.4.1.5826
Perkins, K., Moore, E., Podolefsky, N., Lancaster, K. y Denison, C. (2012). Towards research-based strategies for using PhET simulations in middle school physical science classes, AIP Conference Proceedings, 1413, 295-298. https://doi.org/10.1063/1.3680053
Prendes, M. P., Castañeda, L. y Gutiérrez, I. (2010). Competencias para el uso de TIC de los futuros maestros. Comunicar, 18(35), 175-182. https://doi.org/10.3916/C35-2010-03-11
Ramnarain, U., Pieters, A. y Wu, H. K. (2021). Assessing the technological pedagogical content knowledge of pre-service science teachers at a South African university. International Journal of Information and Communication Technology Education, 17(3), 123-136. https://doi.org/10.4018/
IJICTE.20210701.oa8
Rutten, N., Van Joolingen, W. R. y Van der Veen, J. T. (2012). The learning effects of computer simulations in science education. Computers and Education, 58(1), 136-153. https://doi.org/10.1016/j.compedu.2011.07.017
Sarramona, J. y Santiuste, V. (2015). La formación no presencial ante el reto de las competencias profesionales. Revista Española de Pedagogía, 73, 449-464.
Scalise, K., Timms, M., Moorjani, A., Clark, L., Holtermann, K. e Irvin, P. S. (2011). Student learning in science simulations: design features that promote learning gains. Journal of Research in Science Teaching, 48(9), 1050-1078. https://doi.org/10.1002/tea.20437
Schmidt, D., Baran, E., Thompson, A., Mishra, P., Koehler, M. y Shin, T. (2009). Technological pedagogical content knowledge (TPACK): the development and validation of an assessment instrument for preservice teachers. Journal of Research on Technology in Education, 42(2), 123-149. https://doi.org/10.1080/15391523.2009.10782544
Sierra, F. A. C. y Gutiérrez-Santiuste, E. (2021). Questionnaire on knowledge of university mathematics teachers for technological integration. Revista Fuentes, 2(23), 150-162. https://doi.org/10.12795/revistafuentes.2021.12792
Skjong, R. y Wentworth, B. (2000). Expert judgement and risk perception. http://research.dnv.com/skj/ Papers/SkjWen.pdf
Smetana, L. K. y Bell, R. L. (2012). Computer simulations to support science instruction and learning: a critical review of the literature. International Journal of Science Education, 34(9), 1337-1370. https://doi.org/10.1080/09500693.2011.605182
Smetana, L. K. y Bell, R. L. (2014). Which setting to choose: comparison of whole-class vs. smallgroup computer simulation use. Journal of Science Education and Technology, 23(4), 481-495. https://doi.org/10.1007/s10956-013-9479-z
Sypsas, A. y Kalles, D. (2018). Virtual laboratories in biology, biotechnology and chemistry education: a literature review. En N. Karanikolas y B. Mamalis (eds.), Proceedings of the 22nd Pan-Hellenic Conference on Informatics (PCI ‘18) (70-75). Association for Computing Machinery. https://doi.org/10.1145/3291533.3291560
Tabachnick, B. G. y Fidell, L. S. (2001). Using multivariate statistics. Al-lyn and Bacon.
Taquez, H., Rengifo, D. y Mejía, D. (2017). Diseño de un instrumento para evaluar el nivel de uso y apropiación de las TIC en una institución de educación superior. Portal Educativo de las Américas (OEA). https://recursos.educoas.org/publicaciones/dise-o-de-un-instrumento-para-evaluarel-nivel-de-uso-y-apropiaci-n-de-las-tic-en-una
Tourón, J., Martín, D., Navarro-Asensio, E., Pradas, S. e Íñigo, V. (2018). Validación de constructo de un instrumento para medir la competencia digital docente de los profesores (CDD) | Construct validation of a questionnaire to measure teachers’ digital competence (TDC). Revista Española de Pedagogía, 76(269), 25‑54. https://doi.org/10.22550/REP76‑1‑2018‑02
Wahyuni, I., Zaenuri, Wardono, Sukestiyarno, Y. L., Waluya, S. B., Nuriana y Aminah, N. (2021). Design of instrument Technological Pedagogic Content Knowledge (TPACK) for prospective mathematics teachers. Journal of Physics: Conference Series 1918(4). https://doi.org/10.1088/1742-6596/1918/4/042097
Waight, N., Liu, X., Gregorius, R. M., Smith, E. y Park, M. (2014) Teacher conceptions and approaches associated with an immersive instructional implementation of computer-based models and assessment in a secondary chemistry classroom. International Journal of Science Education, 36(3), 467-505, https://doi.org/10.1080/09500693.2013.787506