Un análisis integrador de las intenciones de los profesores en formación de utilizar herramientas digitales con fines educativos
DOI:
https://doi.org/10.24310/ijtei.112.2025.20960Palabras clave:
herramientas digitales, instrucción, intención, profesor en formación, predictorResumen
Este estudio examina las intenciones de los profesores en formación de adoptar herramientas digitales para las prácticas de instrucción a través del Modelo de Aceptación de la Tecnología (TAM), la Teoría del Comportamiento Planificado (TPB) y el Modelo de Ajuste de la Tecnología a la Tarea (TTF). Se empleó un diseño de investigación transversal para poner a prueba un modelo hipotético a través de una encuesta de autoinforme administrada a los participantes del curso de tecnología educativa. Un total de 210 participantes tomaron parte en la investigación, con los datos de la encuesta recogidos en relación con las opiniones de los profesores en formación sobre la adopción de herramientas de aprendizaje digital en sus futuras prácticas. Se utilizó el modelo de ecuaciones estructurales de mínimos cuadrados parciales (PLS-SEM) para analizar las hipótesis del estudio, y el modelo propuesto arrojó resultados válidos y fiables. El estudio reveló que la adecuación de la tecnología a la tarea era el factor predictivo más influyente en la intención de los profesores en formación de utilizar herramientas digitales de aprendizaje para futuras prácticas docentes. La utilidad percibida y las normas subjetivas fueron otros predictores significativos de la intención conductual, mientras que la actitud y el control conductual percibido no demostraron poder predictivo. En consecuencia, este estudio se suma al cuerpo de conocimientos mediante la introducción de un marco integrado basado en TAM, TPB, y TTF en el contexto de la utilización de la tecnología de los profesores en formación utilizando herramientas digitales de aprendizaje.
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Aivazidi, M., Michalakelis, C., Krouska, A., Sgouropoulou, C., & Cristea, A. I. (2021). Exploring primary school teachers’ intention to use e-learning tools during the COVID-19 pandemic. Education Sciences, 11(11), Article 695. https://doi.org/10.3390/EDUCSCI11110695
Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckman (Eds.), Action-control: From cognition to behavior (pp. 11-39). Springer.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
Ajzen, I. (2001). Nature and operation of attitudes. Annual review of psychology, 52(1), 27-58.
Ajzen, I. (2020). The theory of planned behavior: Frequently asked questions. Human Behavior and Emerging Technologies, 2(4), 314-324. https://doi.org/10.1002/hbe2.195
Almulla, M. A. (2022). Using digital technologies for testing online teaching skills and competencies during the COVID-19 pandemic. Sustainability, 14(9), Article 5455. https://doi.org/10.3390/su14095455
Anderson, S., Groulx, J., & Maninger, R. (2011). Relationships among preservice teachers’ technology-related abilities, beliefs, and intentions to use technology in their future classrooms. Journal of Educational Computing Research, 45(3), 321–338. https://doi.org/10.2190/EC.45.3.d
Ateş, H., & Garzón, J. (2022). Drivers of teachers’ intentions to use mobile applications to teach science. Education and Information Technologies, 27(2), 2521-2542. https://doi.org/10.1007/s10639-021-10671-4
Baber, H. (2021). Modelling the acceptance of e-learning during the pandemic of COVID-19-A study of South Korea. The International Journal of Management Education, 19(2), 100503. https://doi.org/10.1016/j.ijme.2021.100503
Bajaj, P., Khan, A., Tabash, M. I., Anagreh, S., & Koo, A. C. (2021). Teachers’ intention to continue the use of online teaching tools post Covid-19. Cogent Education, 8(1), Article 2002130. https://doi.org/10.1080/2331186X.2021.2002130
Baturay, M. H., Gökçearslan, Ş., & Ke, F. (2017). The relationship among pre-service teachers’ computer competence, attitude towards computer-assisted education, and intention of technology acceptance. International Journal of Technology Enhanced Learning, 9(1), 1–13. https://doi.org/10.1504/IJTEL.2017.084084
Bower, M. (2016). Deriving a typology of Web 2.0 learning technologies. British Journal of Educational Technology, 47(4), 763-777. https://doi.org/10.1111/BJET.12344
Bower, M., & Torrington, J. (2020). Typology of free web-based learning technologies. Educause. https://library.educause.edu/resources/2020/4/typology-of-free-web-based-learning-technologies
Casey, J. E., Pennington, L. K., & Mireles, S. V. (2021). Technology Acceptance Model: Assessing Preservice Teachers’ Acceptance of Floor-Robots as a Useful Pedagogical Tool. Technology, Knowledge and Learning, 26(3), 499-514. https://doi.org/10.1007/s10758-020-09452-8
Chin, W. W. (1998). The partial least squares approach for structural equation modeling. In G. A. Marcoulides (Ed.), Methodology for business and management. Modern methods for business research (pp. 295-336). Erlbaum.
Dahri, N. A., Yahaya, N., Al-Rahmi, W. M., Almogren, A. S., & Vighio, M. S. (2024). Investigating factors affecting teachers’ training through mobile learning: Task technology fit perspective. Education and Information Technologies, 29(12), 14553-14589. https://doi.org/10.1007/S10639-023-12434-9
Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: theory and results. [Doctoral dissertation]. Massachusetts Institute of Technology
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
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.
Eksail, F. A. A., & Afari, E. (2020). Factors affecting trainee teachers’ intention to use technology: A structural equation modeling approach. Education and Information Technologies, 25(4), 2681-2697. https://doi.org/10.1007/s10639-019-10086-2
Ertmer, P. A. (1999). Addressing first- and second-order barriers to change: strategies for technology integration. Educational Technology Research and Development, 47(4), 47-61. https://doi.org/10.1007/BF02299597
Ertmer, P. A., & Ottenbreit-Leftwich, A. T. (2010). Teacher technology change: How knowledge, confidence, beliefs, and culture intersect. Journal of Research on Technology in Education, 42(3), 255-284. https://doi.org/10.1080/15391523.2010.10782551
European Commission. (2017). DigCompEdu framework. https://joint-research-centre.ec.europa.eu/digcompedu/digcompedu-framework_en
Faul, F., Erdfelder, E., Buchner, A., & Lang, A. G. (2009). Statistical power analyses using G* Power 3.1: Tests for correlation and regression analyses. Behavior research methods, 41(4), 1149-1160.
Fishbein, M., & Ajzen, I. (2010). Predicting and changing behavior: The reasoned action approach. Psychology Press.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly: Management Information Systems, 19(2), 213-233. https://doi.org/10.2307/249689
Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572-2593. https://doi.org/10.1111/bjet.12864
Georgiou, D., Trikoili, A., & Kester, L. (2023). Rethinking determinants of primary school teachers’ technology acceptance during the COVID-19 pandemic. Computers and Education Open, 4, 100145. https://doi.org/10.1016/j.caeo.2023.100145
Gurer, M. D. (2021). Examining technology acceptance of pre-service mathematics teachers in Turkey: A structural equation modeling approach. Education and Information Technologies, 26, 4709-4729. https://doi.org/10.1007/s10639-021-10493-4
Habibi, A., Riady, Y., Samed Al-Adwan, A., & Awni Albelbisi, N. (2023). Beliefs and knowledge for pre-service teachers’ technology integration during teaching practice: an extended theory of planned behavior. Computers in the Schools, 40(2), 107-132. https://doi.org/10.1080/07380569.2022.2124752
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139-152. https://doi.org/10.2753/MTP1069-6679190202
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. https://doi.org/10.1108/EBR-11-2018-0203
Haleem, A., Javaid, M., Qadri, M. A., & Suman, R. (2022). Understanding the role of digital technologies in education: A review. Sustainable Operations and Computers, 3, 275-285. https://doi.org/10.1016/j.susoc.2022.05.004
Hew, K. F., & Brush, T. (2007). Integrating technology into K-12 teaching and learning: Current knowledge gaps and recommendations for future research. Educational Technology Research and Development, 55(3), 223-252. https://doi.org/10.1007/s11423-006-9022-5
Hou, M., Lin, Y., Shen, Y., & Zhou, H. (2022). Explaining pre-service teachers’ intentions to use technology-enabled learning: an extended model of the theory of planned behavior. Frontiers in Psychology, 13, Article 900806. https://doi.org/10.3389/FPSYG.2022.900806
Hong, X., Zhang, M., & Liu, Q. (2021). Preschool teachers’ technology acceptance during the COVID-19: an adapted technology acceptance model. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.691492
Inan, F. A., & Lowther, D. L. (2010). Factors affecting technology integration in K-12 classrooms: A path model. Educational Technology Research and Development, 58(2), 137-154. https://doi.org/10.1007/S11423-009-9132-Y
International Society for Technology in Education. (2017). ISTE standards: For educators. https://iste.org/standards/educators
Islamoglu, H., Kabakci Yurdakul, I., & Ursavas, O. F. (2021). Pre-service teachers’ acceptance of mobile-technology-supported learning activities. Educational Technology Research and Development, 69(2), 1025-1054. https://doi.org/10.1007/s11423-021-09973-8
Khong, H., Celik, I., Le, T. T. T., Lai, V. T. T., Nguyen, A., & Bui, H. (2022). Examining teachers’ behavioural intention for online teaching after COVID-19 pandemic: A large-scale survey. Education and Information Technologies, 28, 5999–6026. https://doi.org/10.1007/S10639-022-11417-6
Kim, R., & Song, H.-D. (2022). Examining the influence of teaching presence and task-technology fit on continuance intention to use MOOCs. Asia-Pacific Education Researcher, 31(4), 395-408. https://doi.org/10.1007/s40299-021-00581-x
King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6), 740-755.
Kreijns, K., Van Acker, F., Vermeulen, M., & Van Buuren, H. (2013). What stimulates teachers to integrate ICT in their pedagogical practices? the use of digital learning materials in education. Computers in Human Behavior, 29(1), 217–225. https://doi.org/10.1016/j.chb.2012.08.008
Li, K., Li, Y., & Franklin, T. (2016). Preservice teachers’ intention to adopt technology in their future classrooms. Journal of Educational Computing Research, 54(7), 946–966. https://doi.org/10.1177/0735633116641694
Lee, H. I., Chiu, S. H., Chen, Y. L., Lin, H. Y., & Lin, T. Y. (2022). Determinants of foreign language teachers’ intention to adopt online teaching: an empirical study by the combined model of TAM and TTF. In ACM International Conference Proceeding Series (pp. 249–253). Association for Computing Machinery. https://doi.org/10.1145/3568739.3568781
Lo Presti, A., De Rosa, A., & Viceconte, E. (2021), I want to learn more! Integrating technology acceptance and task–technology fit models for predicting behavioural and future learning intentions. Journal of Workplace Learning, 33(8), 591-605. https://doi.org/10.1108/JWL-11-2020-0179
Lu, H. P., & Yang, Y. W. (2014). Toward an understanding of the behavioral intention to use a social networking site: An extension of task-technology fit to social-technology fit. Computers in Human Behavior, 34, 323-332.
Luik, P., & Taimalu, M. (2021). Predicting the intention to use technology in education among student teachers: A path analysis. Education Sciences, 11(9). https://doi.org/10.3390/educsci11090564
Mathieson, K. (1991). Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2(3), 173-191.
Mei, B., Brown, G. T. L., & Teo, T. (2018). Toward an understanding of preservice english as a foreign language teachers’ acceptance of computer-assisted language learning 2.0 in the people’s republic of China. Journal of Educational Computing Research, 56(1), 74–104. https://doi.org/10.1177/0735633117700144
Milli Eğitim Bakanlığı (2010). Fatih Projesi [Fatih Project]. http://fatihprojesi.meb.gov.tr/
Milli Eğitim Bakanlığı (2017). Öğretmenlik mesleği genel yeterlikleri [General competencies for teaching profession. Directorate General for Teacher Training and Development]. https://oygm.meb.gov.tr/dosyalar/StPrg/Ogretmenlik_Meslegi_Genel_Yeterlikleri.pdf
Müller, W., & Leyer, M. (2023). Understanding intention and use of digital elements in higher education teaching. Education and Information Technologies, 28, 15571-15597. https://doi.org/10.1007/s10639-023-11798-2
Ng, W. (2015). Theories underpinning learning with digital technologies. In New Digital Technology in Education. (pp. 73-94). Springer. https://doi.org/10.1007/978-3-319-05822-1_4
Nunnally J. C., Bernstein I. H. (1994). Psychometric theory (3rd ed.). New York, NY: McGraw-Hill.
Ottenbreit-Leftwich, A., Liao, J. Y. C., Sadik, O., & Ertmer, P. (2018). Evolution of Teachers’ Technology Integration Knowledge, Beliefs, and Practices: How Can We Support Beginning Teachers Use of Technology? Journal of Research on Technology in Education, 50(4), 282-304. https://doi.org/10.1080/15391523.2018.1487350
Paetsch, J., & Drechsel, B. (2021). Factors influencing pre-service teachers’ intention to use digital learning materials: a study conducted during the COVID-19 Pandemic in Germany. Frontiers in Psychology, 12, Article 4953. https://doi.org/10.3389/FPSYG.2021.733830
Pal, D., & Patra, S. (2021). University Students’ perception of video-based learning in times of COVID-19: A TAM/TTF Perspective. International Journal of Human-Computer Interaction, 37(10), 903-921. https://doi.org/10.1080/10447318.2020.1848164
Parkman, S., Litz, D., & Gromik, N. (2018). Examining pre-service teachers’ acceptance of technology-rich learning environments: A UAE case study. Education and Information Technologies, 23(3), 1253-1275. https://doi.org/10.1007/s10639-017-9665-3
Polly, D., Martin, F., & Byker, E. (2022). Examining pre-service and in-service teachers’ perceptions of their readiness to use digital technologies for teaching and learning. Computers in the Schools, 40(1), 22-55. https://doi.org/10.1080/07380569.2022.2121107
Pozo, J. I., Cabellos, B., & del Puy Pérez Echeverría, M. (2024). Has the educational use of digital technologies changed after the pandemic? A longitudinal study. PLOS ONE, 19(12), Article e0311695. https://doi.org/10.1371/JOURNAL.PONE.0311695
Sangeeta, & Tandon, U. (2020). Factors influencing adoption of online teaching by school teachers: A study during COVID-19 pandemic. Journal of Public Affairs, 21(4), Article e2503. https://doi.org/10.1002/pa.2503
Sharma, S., & Saini, J. R. (2022). On the role of teachers’ acceptance, continuance intention and self-efficacy in the use of digital technologies in teaching practices. Journal of Further and Higher Education, 46(6), 721-736. https://doi.org/10.1080/0309877X.2021.1998395
Songkram, N., & Osuwan, H. (2022). Applying the technology acceptance model to elucidate k-12 teachers’ use of digital learning platforms in Thailand during the COVID-19 pandemic. Sustainability, 14(10), Article 6027. https://doi.org/10.3390/su14106027
Şimşek, A. S., & Ateş, H. (2022). The extended technology acceptance model for Web 2.0 technologies in teaching. Innoeduca. International Journal of Technology and Educational Innovation, 8(2), 165-183. https://doi.org/10.24310/INNOEDUCA.2022.V8I2.15413
Tang, K. Y., Hsiao, C. H., Tu, Y. F., Hwang, G. J., & Wang, Y. (2021). Factors influencing university teachers’ use of a mobile technology-enhanced teaching (MTT) platform. Educational Technology Research and Development, 69(5), 2705–2728. https://doi.org/10.1007/s11423-021-10032-5
Taylor, M., Fudge, A., Mirriahi, N., & De Laat, M. (2021). Use of digital technology in education: literature review. Centre for Change and Complexity in Learning, The University of South Australia. https://www.education.sa.gov.au/docs/ict/digital-strategy-microsite/c3l-digital-technologies-in-education-literature-review.pdf
Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144-176. https://doi.org/10.1287/isre.6.2.144
Teo, T. (2012). Examining the intention to use technology among pre-service teachers: An integration of the Technology Acceptance Model and Theory of Planned Behavior. Interactive Learning Environments, 20(1), 3-18. https://doi.org/10.1080/10494821003714632
Teo, T., Fan, X., & Du, J. (2015). Technology acceptance among pre-service teachers: Does gender matter? Australasian Journal of Educational Technology, 31(3), 235-251. https://doi.org/10.14742/AJET.1672
Teo, T., Huang, F., & Hoi, C. K. W. (2018). Explicating the influences that explain intention to use technology among English teachers in China. Interactive Learning Environments, 26(4), 460-475. https://doi.org/10.1080/10494820.2017.1341940
Teo, T., & Lee, C. B. (2010). Explaining the intention to use technology among student teachers An application of the Theory of Planned Behavior (TPB). Campus-Wide Information Systems, 27(2), 60-67. https://doi.org/10.1108/10650741011033035
Teo, T., Sang, G., Mei, B., & Hoi, C. K. W. (2019). Investigating pre-service teachers’ acceptance of Web 2.0 technologies in their future teaching: a Chinese perspective. Interactive Learning Environments, 27(4), 530-546. https://doi.org/10.1080/10494820.2018.1489290
Teo, T., & Tan, L. (2012). The theory of planned behavior (TPB) and pre-service teachers’ technology acceptance: A validation study using structural equation modeling. Journal of Technology and Teacher Education, 20(1), 89-104.
Teo, T., Ursavaş, Ö. F., & Bahçekapili, E. (2011). Efficiency of the technology acceptance model to explain pre‐service teachers' intention to use technology: A Turkish study. Campus-Wide Information Systems, 28(2), 93-101. https://doi.org/10.1108/10650741111117798
Teo, T., & van Schaik, P. (2012). Understanding the Intention to Use Technology by Preservice Teachers: An Empirical Test of Competing Theoretical Models. International Journal of Human-Computer Interaction, 28(3), 178-188. https://doi.org/10.1080/10447318.2011.581892
Teo, T., Zhou, M., Fan, A. C. W., & Huang, F. (2019). Factors that influence university students’ intention to use Moodle: A study in Macau. Educational Technology Research and Development, 67, 749-766. https://doi.org/10.1007/s11423-019-09650-x
Teo, T., Zhou, M., & Noyes, J. (2016). Teachers and technology: Development of an extended theory of planned behavior. Educational Technology Research and Development, 64, 1033-1052.
Timotheou, S., Miliou, O., Dimitriadis, Y., Villagrá Sobrino, S., Giannoutsou, N., Cachia, R., Martínez Monés, A., & Ioannou, A. (2023). Impacts of digital technologies on education and factors influencing schools’ digital capacity and transformation: A literature review. Education and Information Technologies, 28(6), 6695-6726. https://doi.org/10.1007/s10639-022-11431-8
Ursavaş, Ö. F., Yalçın, Y., & Bakır, E. (2019). The effect of subjective norms on preservice and in-service teachers’ behavioural intentions to use technology: A multigroup multimodel study. British Journal of Educational Technology, 50(5), 2501–2519. https://doi.org/10.1111/bjet.12834
Valtonen, T., Kukkonen, J., Kontkanen, S., Mäkitalo‐Siegl, K., & Sointu, E. (2018). Differences in pre‐service teachers' knowledge and readiness to use ICT in education. Journal of Computer Assisted Learning, 34(2), 174-182.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly: Management Information Systems, 27(3), 425-478. https://doi.org/10.2307/30036540
Wang, Y. (2022). A comparative study of Chinese and American preservice teachers’ intention to teach online based on the Theory of Planned Behavior. Education and Information Technologies, 28, 6391-6405. https://doi.org/10.1007/s10639-022-11442-5
Wang, C., Chen, X., Yu, T., Liu, Y., & Jing, Y. (2024). Education reform and change driven by digital technology: a bibliometric study from a global perspective. Humanities and Social Sciences Communications, 11, Article 256. https://doi.org/10.1057/s41599-024-02717-y
Watson, J. H., & Rockinson-Szapkiw, A. (2021). Predicting preservice teachers’ intention to use technology-enabled learning. Computers & Education, 168, Article 104207. https://doi.org/10.1016/J.COMPEDU.2021.104207
Wilson, M. L., Ritzhaupt, A. D., & Cheng, L. (2020). The impact of teacher education courses for technology integration on pre-service teacher knowledge: A meta-analysis study. Computers & Education, 156, Article 103941. https://doi.org/10.1016/J.COMPEDU.2020.103941
Wohlfart, O., & Wagner, I. (2024). Longitudinal perspectives on technology acceptance: Teachers’ integration of digital tools through the COVID-19 transition. Education and Information Technologies. Advance online publication. https://doi.org/10.1007/S10639-024-12954-Y
Wong, K. T., Teo, T., & Russo, S. (2013). Interactive Whiteboard acceptance: applicability of the UTAUT model to student teachers. Asia-Pacific Education Researcher, 22(1), 1–10. https://doi.org/10.1007/s40299-012-0001-9
Wu, B., & Chen, X. (2017). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior, 67, 221-232. https://doi.org/10.1016/j.chb.2016.10.028
Wu, R., & Yu, Z. (2022). The Influence of social isolation, technostress, and personality on the acceptance of online meeting platforms during the COVID-19 pandemic. International Journal of Human–Computer Interaction, 39(17), 3388-3405. https://doi.org/10.1080/10447318.2022.2097779
Yükseköğretim Kurulu. (2018). Yeni öğretmen yetiştirme lisans programları [New teacher training undergraduate programs]. https://www.yok.gov.tr/kurumsal/idari-birimler/egitim-ogretim-dairesi/yeni-ogretmen-yetistirme-lisans-programlari
Yusop, F. D., Habibi, A., & Razak, R. A. (2021). Factors Affecting Indonesian Preservice Teachers’ Use of ICT During Teaching Practices Through Theory of Planned Behavior. SAGE Open, 11(2). https://doi.org/10.1177/21582440211027572
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