Factores determinantes de la lealtad y la finalización en MOOCs para el desarrollo profesional del profesorado: un modelo integrado de aceptación tecnológica
Resumen
El desarrollo profesional del profesorado es un factor clave para mejorar la calidad de los sistemas educativos. El e-learning y especialmente los cursos masivos online están adquiriendo un gran peso como soporte para esta formación, ya que permiten superar muchas de las barreras de acceso a la formación y fomentar el aprendizaje cooperativo. Aunque los aspectos que influyen en el éxito de los MOOC están ampliamente estudiados, las características específicas del profesorado como aprendiz dificultan la transferibilidad de esos resultados. Consecuentemente, este trabajo estudia qué variables explican la lealtad y el grado de terminación de un MOOC dirigido a profesorado no universitario. El modelo planteado integra variables relacionadas con la aceptación de la tecnología, incluyendo el estado de flujo, y variables personales como la ansiedad y la autoeficacia. Nuestros resultados indican que, para el profesorado, la variable clave de todo el sistema es la utilidad percibida y la satisfacción con el aprendizaje, teniendo la facilidad de uso una relevancia menor. Al contrario que lo descrito en la literatura, no hay diferencias resaltables relativas al sexo. Nuestro modelo predice adecuadamente la formación de la lealtad, pero persiste la dificultad de explicar el uso efectivo.
Descargas
-
Resumen267
-
pdf61
-
pdf EN61
Citas
Abdous, M. H. (2019). Influence of satisfaction and preparedness on online students’ feelings of anxiety. The Internet and Higher Education, 41, 34–44.
Abdullah, F., & Ward, R. (2016). Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analysing commonly used external factors. Computers in human behavior, 56, 238-256.
Appova, A., & Arbaugh, F. (2018). Teachers’ motivation to learn: Implications for supporting professional growth. Professional development in education, 44(1), 5-21.
Arquero, J. L., del Barrio-García, S., & Romero-Frías, E. (2017). What drives students' loyalty-formation in social media learning within a personal learning environment approach? The moderating role of need for cognition. Journal of Educational Computing Research, 55(4), 495-525.
Arquero, J. L., Romero-Frías, E., & Del Barrio-García, S. (2022). The impact of flow, satisfaction and reputation on loyalty to MOOCs: the moderating role of extrinsic motivation. Technology, Pedagogy and Education, 31(4), 399-415.
Arteaga Sánchez, R., & Duarte Hueros, A. (2010). Motivational factors that influence the acceptance of Moodle using TAM. Computers in human behavior, 26(6), 1632-1640.
Bandura, A. (2006). Guide for constructing self-efficacy scales. In F. Pajares & T. Urdan (Eds.), Self-Efficacy Beliefs of Adolescents, (pp. 307–337). Greenwich: Information Age Publishing.
Barbeite, F. G., & Weiss, E. M. (2004). Computer self-efficacy and anxiety scales for an Internet sample: testing measurement equivalence of existing measures and development of new scales. Computers in human behavior, 20(1), 1-15.
Becker, J.-M., Ringle, C. & Sarstedt, M. (2018). Estimating moderating effects in PLS-SEM and PLSc-SEM: Interaction term generation data treatment. Journal of Applied Structural Equation Modeling, 2(2), 1-21.
Brugha, M. E., Arif, I., Peters, S., Ahmed, F., Piccini, C., Bermudez, G. M., ... & Weeden, K. (2024). Educators’ Perceptions and Experiences of Online Teacher Professional Development. Journal of Interactive Media in Education, 2024(1): 17, 1-15.
Calisir, F., Altin Gumussoy, C., Bayraktaroglu, A. E., & Karaali, D. (2014). Predicting the intention to use a web‐based learning system: Perceived content quality, anxiety, perceived system quality, image, and the technology acceptance model. Human Factors and Ergonomics in Manufacturing & Service Industries, 24(5), 515-531.
Castaño-Muñoz, J., Kalz, M., Kreijns, K., & Punie, Y. (2018). Who is taking MOOCs for teachers’ professional development on the use of ICT? A cross-sectional study from Spain. Technology, Pedagogy and Education, 27(5), 607-624.
Castaño-Muñoz, J., Kreijns, K., Kalz, M., & Punie, Y. (2017). Does digital competence and occupational setting influence MOOC participation? Evidence from a cross-course survey. Journal of Computing in Higher Education, 29, 28-46.
Chen, B., Fan, Y., Zhang, G., Liu, M., & Wang, Q. (2020). Teachers’ networked professional learning with MOOCs. PloS one, 15(7), e0235170.
Chua, S. L., Chen, D. T., & Wong, A. F. (1999). Computer anxiety and its correlates: a meta-analysis. Computers in human behavior, 15(5), 609-623.
Del Barrio-García, S., Arquero, J. L., & Romero-Frías, E. (2015). Personal learning environments acceptance model: The role of need for cognition, e-learning satisfaction and students’ perceptions. Educational Technology and Society, 18(3), 129–141.
Deshpande, A., & Chukhlomin, V. (2017). What Makes a Good MOOC: A Field Study of Factors Impacting Student Motivation to Learn. American Journal of Distance Education, 31(4), 275-293.
Elizondo-García, J., & Gallardo, K. (2020). Peer Feedback in Learner Learner Interaction Practices. Mixed Methods Study on an xMOOC. Electronic Journal of E-learning, 18(2), 122-135.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39–50.
Goswami, A., & Dutta, S. (2015). Gender differences in technology usage—A literature review. Open Journal of Business and Management, 4(1), 51-59.
Hair, J. F., Risher, J. J., Sarstedt, M. & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24.
Hattie, J. (2015). The applicability of visible learning to higher education. Scholarship of Teaching and Learning, 1(1), 79–91.
Henseler, J., Ringle, C. M. & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135.
Herranen, J. K., Aksela, M. K., Kaul, M., & Lehto, S. (2021). Teachers’ expectations and perceptions of the relevance of professional development MOOCs. Education Sciences, 11(5), 240, 1-11.
Hertz, B., Grainger Clemson, H., Tasic Hansen, D., Laurillard, D., Murray, M., Fernandes, L., ... & Rutkauskiene, D. (2022). A pedagogical model for effective online teacher professional development—findings from the Teacher Academy initiative of the European Commission. European Journal of Education, 57(1), 142-159.
Ho, A., Chuang, I., Coleman, C., Whitehill, J., Northcutt, C., Williams, J. J., Hansen, J., Lopez, G., & Peterson, R. (2015). HarvardX and MITx: Two years of massive open online courses. Fall 2012–Summer 2014. Recuperado de: https://ssrn.com/abstract=2586847
Huang, C. (2018) Generating New Paths for Teacher Professional Development (TPD) through MOOCs. Journal of Educational Research and Development, 14(1), 35-71.
Joo, Y. J., So, H. J., & Kim, N. H. (2018). Examination of relationships among students' self-determination, technology acceptance, satisfaction, and continuance intention to use K-MOOCs. Computers & Education, 122, 260-272.
Koukis, N., & Jimoyiannis, A. (2019). MOOCS for teacher professional development: exploring teachers’ perceptions and achievements. Interactive Technology and Smart Education, 16(1), 74-91.
Lakhal, S., & Khechine, H. (2021). Technological factors of students’ persistence in online courses in higher education: The moderating role of gender, age and prior online course experience. Education and Information Technologies, 26(3), 3347-3373.
Laurillard, D. (2016). The educational problem that MOOCs could solve: Professional development for teachers of disadvantaged students. Research in Learning Technology, 24(1), 29369.
Lee, J. W. (2010). Online support service quality, online learning acceptance, and student satisfaction. The internet and higher education, 13(4), 277-283.
Lee, Y. F., Lin, C. J., Hwang, G. J., Fu, Q. K., & Tseng, W. H. (2023). Effects of a mobile-based progressive peer-feedback scaffolding strategy on students’ creative thinking performance, metacognitive awareness, and learning attitude. Interactive Learning Environments, 31(5), 2986-3002.
Lee, Y., & Choi, J. (2011). A review of online course dropout research: Implications for practice and future research. Educational Technology Research and Development, 59(5), 593–618.
Liu, I. F., Chen, M. C., Sun, Y. S., Wible, D., & Kuo, C. H. (2010). Extending the TAM model to explore the factors that affect intention to use an online learning community. Computers & education, 54(2), 600-610.
Ma, N., Li, Y. M., Guo, J. H., Laurillard, D., & Yang, M. (2023). A learning model for improving in-service teachers’ course completion in MOOCs. Interactive Learning Environments, 31(9), 5940-5955.
Ministerio de Educación y Formación Profesional (2019). TALIS 2018 Estudio internacional de la enseñanza y del aprendizaje. Informe español. Instituto Nacional de Evaluación Educativa.
Ministerio de Educación y Formación Profesional (2023). Sistema estatal de indicadores de la educación 2023. Instituto Nacional de Evaluación Educativa.
Misra, P. (2018). MOOCs for teacher professional development: Reflections and suggested actions. Open Praxis, 10(1), 67-77.
Mulik, S., Srivastava, M., Yajnik, N., & Taras, V. (2020). Antecedents and outcomes of flow experience of MOOC users. Journal of International Education in Business, 13(1), 1-19.
Ngai, E. W. T., Poon, J. K. L., & Chan, Y. H. C. (2007). Empirical examination of the adoption of WebCT using TAM. Computers and Education, 48 (2), 250-67.
OECD (2009), Creating Effective Teaching and Learning Environments: First Results from TALIS, TALIS, OECD Publishing, Paris.
OECD (2010). PISA 2009 results: What makes a school successful? OECD Publishing. Paris.
OECD (2014), A Teachers' Guide to TALIS 2013: Teaching and Learning International Survey, TALIS, OECD Publishing, Paris.
OECD (2019), TALIS 2018 Results (Volume I): Teachers and School Leaders as Lifelong Learners, TALIS, OECD Publishing, Paris.
Park, S. Y., Nam, M. W., & Cha, S. B. (2012). University students' behavioral intention to use mobile learning: Evaluating the technology acceptance model. British Journal of Educational Technology, 43(4), 592–605.
Rabin, E., Henderikx, M., Yoram, M. K., & Kalz, M. (2020). What are the barriers to learners’ satisfaction in MOOCs and what predicts them? The role of age, intention, self-regulation, self-efficacy and motivation. Australasian Journal of Educational Technology, 36(3), 119-131.
Ringle, C. M., Wende, S., and Becker, J.M.( 2022). SmartPLS 4. Oststeinbek: SmartPLS GmbH, http://www.smartpls.com.
Romero-Frías, E., Arquero, J.L., & del Barrio-García, S. (2023). Exploring how student motivation relates to acceptance and participation in MOOCs. Interactive Learning Environments. 31(1), 480-496.
Siemens, G. (2005). Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning, 2(1), 3–10.
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.
Yousafzai, S. Y., Foxall, G. R., & Pallister, J. G. (2007). Technology acceptance: a meta‐analysis of the TAM: Part 2. Journal of Modelling in Management, 2(3), 281-304.
Yuen, A. H., & Ma, W. W. (2002). Gender differences in teacher computer acceptance. Journal of technology and Teacher Education, 10(3), 365-382.
Los artículos que se publican en esta revista están sujetos a los siguientes términos:
1. El Departamento de Métodos de Investigación y Diagnóstico en Educación de la Universidad de Murcia (España), junto con el Servicio de Publicaciones de la Universitdad de Murcia (Editum) son los editores de la revista REIFOP y conserva los derechos patrimoniales (copyright) de los artículos publicados, permitiendo la reutilización de las mismos bajo la licencia de uso indicada en el punto 2.
2. Las obras se publican en la edición electrónica de la revista bajo una licencia Creative Commons Reconocimiento-NoComercial-SinObraDerivada 3.0 España (texto legal). Se pueden copiar, usar, difundir, transmitir y exponer públicamente, siempre que: i) se cite la autoría y la fuente original de su publicación (revista, editores y URL de la obra); ii) no se usen para fines comerciales; iii) se mencione la existencia y especificaciones de esta licencia de uso.
3. Condiciones de auto-archivo. Se permite y se anima a los autores a difundir electrónicamente las versiones pre-print (versión antes de ser evaluada) y/o post-print (versión evaluada y aceptada para su publicación) de sus obras antes de su publicación, ya que favorece su circulación y difusión más temprana y con ello un posible aumento en su citación y alcance entre la comunidad académica. Color RoMEO: verde.












