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Hybrid Regression Analysis of Predictors of Teachers' ICT Use in Teaching Practices: Evidence from ICILS

Yıl 2025, Cilt: 12 Sayı: 2, 412 - 427

Kaan Batı , Şeyma Irmak

Öz

The effective integration of Information and Communication Technology (ICT) in education is crucial for student success in the digital age, and large-scale international assessments provide valuable data for understanding and improving these practices. This study investigates the factors influencing teachers' ICT integration in teaching using the ICILS 2018 dataset. The study combines traditional methods, such as multiple linear regression, with machine learning techniques like the random forest algorithm, to comprehensively understand the factors driving teachers' ICT use. The findings reveal that the models successfully explained a significant portion of the variance in teachers' ICT practices. The random forest analysis identified country context as the most influential predictor, followed by teachers' positive views on the benefits of ICT, teacher collaboration, ICT self-efficacy, and experience with online lessons. Factors such as experience in preparing online materials and teacher age had a moderate influence, while access to computer resources and gender showed minimal impact. The results encourage educators and policymakers to invest in modifiable factors, such as teacher collaboration, self-efficacy, and positive perceptions of ICT, to enhance teachers' ICT integration.

Anahtar Kelimeler

ICILS ICT multiple linear regression random forest teachers UTAUT

Etik Beyan

In this study, we declare that the rules stated in the "Higher Education Institutions Scientific Research and Publication Ethics Directive" are complied with and that we do not take any of the actions based on "Actions Against Scientific Research and Publication Ethics". At the same time, we declare that there is no conflict of interest between the authors, which all authors contribute to the study, and that all the responsibility belongs to the article authors in case of all ethical violations.

Kaynakça

  • 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
  • Ayanwale, M.A., Molefi, R.R. & Oyeniran, S. (2024). Analyzing the evolution of machine learning integration in educational research: a bibliometric perspective. Discovery Education, 3(1), 47. https://doi.org/10.1007/s44217-024-00119-5
  • Baako, I., & Abroampa, W. K. (2024). Context matters: Exploring teacher and learner contexts in ICT integration in slum public basic schools in Ghana. Cogent Education, 11(1), 2342637. https://doi.org/10.1080/2331186X.2024.2342637
  • Baek, Y., Jung, J., & Kim, B. (2008). What makes teachers use technology in the classroom? Exploring the factors affecting facilitation of technology with a Korean sample. Computers & Education, 50(1), 224-234. https://doi.org/10.1016/j.compedu.2006.05.002
  • Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall. Behrendt, P. (2014). lm.beta (Version 1.5-1) [Computer software]. The Comprehensive R Archive Network (CRAN). https://CRAN.R-project.org/package=lm.beta
  • Blackwell, C. K., Lauricella, A. R., & Wartella, E. (2014). Factors influencing digital technology use in early childhood education. Computers & Education, 77, 82-90. https://doi.org/10.1016/j.compedu.2014.04.013
  • Bingimlas, K. A. (2009). Barriers to the successful integration of ICT in teaching and learning environments: A review of the literature. Eurasia Journal of Mathematics, Science and Technology Education, 5(3), 235-245.
  • Bocconi, S., Chioccariello, A., & Earp, J. (2018). The Nordic approach to introducing computational thinking and programming in compulsory education. European Schoolnet. https://doi.org/10.17471/54007
  • Breiman, L. (2001). Random forests. Machine Learning, 45, 5-32.
  • Brenes-Monge, M. M., Fernández-Martínez, M. D. M., Pérez-Esteban, M. D., & Carrión-Martínez, J. J. (2020). Teacher and context factors associated with the educational use of ICT: A Costa Rican case study. Sustainability, 12(23), 10170. https://doi.org/10.3390/su122310170
  • ChanLin, L. J., Hong, J. C., Horng, J. S., Chang, S. H., & Chu, H. C. (2006). Factors influencing technology integration in teaching: A Taiwanese perspective. Innovations in Education and Teaching International, 43(1), 57-68. https://doi.org/10.1080/14703290500467467
  • Chen, X. (2023). The impact of perceived teacher IT use on information literacy among Chinese secondary school students. International Journal of Learning and Teaching, 9(4), 381–390. https://doi.org/10.18178/ijlt.9.4.381-390
  • Cohen, J. (2013). Statistical power analysis for the behavioral sciences (2nd ed.). Routledge. https://doi.org/10.4324/9780203771587
  • Davis, F. D. (1989). Technology acceptance model: TAM. In M. N. Al-Suqri & A. S. Al-Aufi (Eds.), Information seeking behavior and technology adoption (pp. 205 - 219). University of Michigan Press.
  • Ertmer, P. A., Ottenbreit-Leftwich, A. T., Sadik, O., Sendurur, E., & Sendurur, P. (2012). Teacher beliefs and technology integration practices: A critical relationship. Computers & Education, 59(2), 423-435. https://doi.org/10.1016/j.compedu.2012.02.001
  • Fraillon, J.S.R., Liaw, Y.-L., Meinck, S., Wild, J., Christensen, J., Hughes, C., Leino, K., Rozman, M., & Cortés, D. (2021). Changes in Digital Learning During a Pandemic: Findings From the ICILS Teacher Panel. International Association for the Evaluation of Educational Achievement.
  • Gómez-Trigueros, I. M., & Yáñez de Aldecoa, C. (2021). The Digital Gender Gap in Teacher Education: The TPACK Framework for the 21st Century. European Journal of Investigation in Health, Psychology and Education, 11(4), 1333-1349. https://doi.org/10.3390/ejihpe11040097 Hall, G. E. (1974). The concerns-based adoption model: A developmental conceptualization of the adoption process within educational institutions. The Research and Development Center for Teacher Education, the University of Texas at Austin. Retriewed from: https://files.eric.ed.gov/fulltext/ED111791.pdf
  • Hatlevik, O. E. (2017). Examining the relationship between teachers’ self-efficacy, their digital competence, strategies to evaluate information and use of ICT at school. Scandinavian Journal of Educational Research, 61(5), 555-567.
  • Ibieta, A., Hinostroza, J. E., Labbé, C., & Claro, M. (2017). The role of the Internet in teachers’ professional practice: activities and factors associated with teacher use of ICT inside and outside the classroom.
  • Technology, Pedagogy and Education, 26(4), 425-438. https://doi.org/10.1080/1475939X.2017.1296489 ISTE. (2017). ISTE standards for educators. International Society for Technology in Education. Retrieved from https://www.iste.org/standards/for-educators
  • Kafyulilo, A., Fisser, P., & Voogt, J. (2016). Factors affecting teachers’ continuation of technology use in teaching. Education and Information Technologies, 21, 1535-1554. https://doi.org/10.1007/s10639-015-9398-0
  • Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R News, 2(3), 18–22. https://CRAN.R-project.org/package=randomForest
  • Liu, Y., Wang, Y., & Zhang, J. (2012). New machine learning algorithm: Random forest. In B. Liu, M. Ma, & J. Chang (Eds.), Information computing and applications: ICICA 2012 (Lecture notes in computer science, Vol. 7473, pp. 246–252). Berlin, Germany: Springer. https://doi.org/10.1007/978-3-642-34062-8_32
  • Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., & Leisch, F. (2019). e1071: Misc functions of the Department of Statistics, Probability Theory Group (Version 1.7-3) [Computer software]. The Comprehensive R Archive Network (CRAN). https://CRAN.R-project.org/package=e1071
  • Moon, T. K. (1996). The expectation-maximization algorithm. IEEE Signal Processing Magazine, 13(6), 47-60.
  • Mirzajani, H., Mahmud, R., Fauzi Mohd Ayub, A., & Wong, S. L. (2016). Teachers’ acceptance of ICT and its integration in the classroom. Quality Assurance in Education, 24(1), 26-40. https://doi.org/10.1108/QAE-06-2014-0025
  • Mumtaz, S. (2000). Factors affecting teachers' use of information and communications technology: A review of the literature. Journal of Information Technology for Teacher Education, 9(3), 319-342. https://doi.org/10.1080/14759390000200096
  • Müller, K., & Wickham, H. (2018). tibble: Simple data frames (Version 2.1.3) [Computer software]. The Comprehensive R Archive Network (CRAN). https://CRAN.R-project.org/package=tibble
  • Nikou, S. A., & Economides, A. A. (2017). Mobile-based assessment: Investigating the factors that influence behavioral intention to use. Computers & Education, 109, 56-73. https://doi.org/10.1016/j.compedu.2017.02.005
  • Noureddine, R., Boote, D., & Campbell, L. O. (2025). Assessing the validity of UTAUT among higher education instructors: A meta-analysis. Education and Information Technologies, 30, 16687–16719 (2025). https://doi.org/10.1007/s10639-025-13449-0
  • Organisation for Economic Co-operation and Development. (n.d.). Science, technology and innovation. Retrieved January 5, 2025, from https://www.oecd.org/en/topics/science-technology-and-innovation.html
  • Oliveira, T., Faria, M., Thomas, M. A., & Popovič, A. (2014). Extending the understanding of mobile banking adoption: When UTAUT meets TTF and ITM. International Journal of Information Management, 34(5), 689–703. https://doi.org/10.1016/j.ijinfomgt.2014.06.004
  • Peng, R., Abdul Razak, R., & Hajar Halili, S. (2023). Factors influencing in-service teachers’ technology integration model: Innovative strategies for educational technology. PloS One, 18(8), e0286112. https://doi.org/10.1371/journal.pone.0286112
  • Punie, Y., & Redecker, C. (2017) European Framework for the Digital Competence of Educators: DigCompEdu , Publications Office of the European Union, Luxembourg. https://doi.org/10.2760/159770
  • R Core Team. (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/
  • Revelle, W. (2020). psych: Procedures for psychological, psychometric, and personality research (Version 2.0.7) [Computer software]. The Comprehensive R Archive Network (CRAN). https://CRAN.R-project.org/package=psych
  • Rogers, E. (2003). Diffusion of innovations (5th ed.). Free Press.
  • 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. https://doi.org/10.1016/j.compedu.2018.09.009
  • Seth, M. (2020). writexl: Read and write Excel files (Version 1.3.1) [Computer software]. The Comprehensive R Archive Network (CRAN). https://CRAN.R-project.org/package=writexl
  • Shoraevna, Z., Eleupanovna, Z., Tashkenbaevna, S., Zulkarnayeva, Z., Anatolevna, L., & Nurlanbekovna, U. (2021). Teachers’ views on the use of Information and Communication Technologies (ICT) in education environments. International Journal of Emerging Technologies in Learning (iJET), 16(3), 261–273. https://doi.org/10.3991/ijet.v16i03.18801
  • Speiser, J. L., Miller, M. E., Tooze, J., & Ip, E. (2019). A comparison of random forest variable selection methods for classification prediction modeling. Expert Systems with Applications, 134, 93-101. https://doi.org/10.1016/j.eswa.2019.05.028
  • Spiteri, M., & Chang Rundgren, S. N. (2020). Literature review on the factors affecting primary teachers’ use of digital technology. Technology, Knowledge and Learning, 25(1), 115-128. https://doi.org/10.1007/s10758-018-9376-x
  • Teo, T., & Noyes, J. (2012). Explaining the intention to use technology among pre-service teachers: a multi-group analysis of the Unified Theory of Acceptance and Use of Technology. Interactive Learning Environments, 22(1), 51–66. https://doi.org/10.1080/10494820.2011.641674
  • Tierney, N., Cook, D., McBain, M., & Fay, C. (2020). naniar: Data structures, summaries, and visualizations for missing data (Version 0.6.0) [Computer software]. The Comprehensive R Archive Network (CRAN). https://CRAN.R-project.org/package=naniar
  • Tomczyk, Ł., Costas Jáuregui, V., Albuquerque de La Higuera Amato, C., Muñoz, D., Arteaga, M., Oyelere, S. S., Akyar, Ö. Y., & Porta, M. (2021). Are teachers techno-optimists or techno-pessimists? A pilot comparative study among teachers in Bolivia, Brazil, the Dominican Republic, Ecuador, Finland, Poland, Turkey, and Uruguay. Education and Information Technologies, 26, 2715–2741. https://doi.org/10.1007/s10639-020-10380-4
  • United Nations Educational, Scientific and Cultural Organization. (2018). UNESCO ICT competency framework for teachers. Paris, France: Author. Retrieved from https://unesdoc.unesco.org/ark:/48223/pf0000265721
  • United Nations Educational, Scientific and Cultural Organization. (2021). Reimagining our futures together: A new social contract for education. Paris, France: Author. Retrieved from https://unesdoc.unesco.org/ark:/48223/pf0000379707
  • U.S. Department of Education. (n.d.). National Education Technology Plan (NETP). Retrieved from https://tech.ed.gov/netp/
  • U.S. Trade and Development Agency. (2023). Uruguay - Education and training sector snapshot. Retrieved from https://www.trade.gov/country-commercial-guides/uruguay-education-and-training-sector-snapshot
  • 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.
  • Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology (UTAUT2). MIS Quarterly, 36(1), 157–178.
  • Wickham, H., & Chang, W. (2014). ggplot2: An implementation of the grammar of graphics (Version 1.0.0) [Computer software]. http://ggplot2.org
  • Wickham, H., et al. (2019). tidyverse: Easily install and load the 'Tidyverse' (Version 1.3.0) [Computer software]. https://cran.r-project.org/web/packages/tidyverse/tidyverse.pdf
  • Wickham, H., & Bryan, J. (2019). readxl: Read Excel files (Version 1.3.1) [Computer software]. https://cran.r-project.org/web/packages/readxl/readxl.pdf
  • Wickham, H., & Henry, L. (2020). tidyr: Tidy messy data (Version 1.1.2) [Computer software]. https://cran.r-project.org/web/packages/tidyr/tidyr.pdf
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Yıl 2025, Cilt: 12 Sayı: 2, 412 - 427

Kaan Batı , Şeyma Irmak

Öz

Kaynakça

  • 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
  • Ayanwale, M.A., Molefi, R.R. & Oyeniran, S. (2024). Analyzing the evolution of machine learning integration in educational research: a bibliometric perspective. Discovery Education, 3(1), 47. https://doi.org/10.1007/s44217-024-00119-5
  • Baako, I., & Abroampa, W. K. (2024). Context matters: Exploring teacher and learner contexts in ICT integration in slum public basic schools in Ghana. Cogent Education, 11(1), 2342637. https://doi.org/10.1080/2331186X.2024.2342637
  • Baek, Y., Jung, J., & Kim, B. (2008). What makes teachers use technology in the classroom? Exploring the factors affecting facilitation of technology with a Korean sample. Computers & Education, 50(1), 224-234. https://doi.org/10.1016/j.compedu.2006.05.002
  • Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall. Behrendt, P. (2014). lm.beta (Version 1.5-1) [Computer software]. The Comprehensive R Archive Network (CRAN). https://CRAN.R-project.org/package=lm.beta
  • Blackwell, C. K., Lauricella, A. R., & Wartella, E. (2014). Factors influencing digital technology use in early childhood education. Computers & Education, 77, 82-90. https://doi.org/10.1016/j.compedu.2014.04.013
  • Bingimlas, K. A. (2009). Barriers to the successful integration of ICT in teaching and learning environments: A review of the literature. Eurasia Journal of Mathematics, Science and Technology Education, 5(3), 235-245.
  • Bocconi, S., Chioccariello, A., & Earp, J. (2018). The Nordic approach to introducing computational thinking and programming in compulsory education. European Schoolnet. https://doi.org/10.17471/54007
  • Breiman, L. (2001). Random forests. Machine Learning, 45, 5-32.
  • Brenes-Monge, M. M., Fernández-Martínez, M. D. M., Pérez-Esteban, M. D., & Carrión-Martínez, J. J. (2020). Teacher and context factors associated with the educational use of ICT: A Costa Rican case study. Sustainability, 12(23), 10170. https://doi.org/10.3390/su122310170
  • ChanLin, L. J., Hong, J. C., Horng, J. S., Chang, S. H., & Chu, H. C. (2006). Factors influencing technology integration in teaching: A Taiwanese perspective. Innovations in Education and Teaching International, 43(1), 57-68. https://doi.org/10.1080/14703290500467467
  • Chen, X. (2023). The impact of perceived teacher IT use on information literacy among Chinese secondary school students. International Journal of Learning and Teaching, 9(4), 381–390. https://doi.org/10.18178/ijlt.9.4.381-390
  • Cohen, J. (2013). Statistical power analysis for the behavioral sciences (2nd ed.). Routledge. https://doi.org/10.4324/9780203771587
  • Davis, F. D. (1989). Technology acceptance model: TAM. In M. N. Al-Suqri & A. S. Al-Aufi (Eds.), Information seeking behavior and technology adoption (pp. 205 - 219). University of Michigan Press.
  • Ertmer, P. A., Ottenbreit-Leftwich, A. T., Sadik, O., Sendurur, E., & Sendurur, P. (2012). Teacher beliefs and technology integration practices: A critical relationship. Computers & Education, 59(2), 423-435. https://doi.org/10.1016/j.compedu.2012.02.001
  • Fraillon, J.S.R., Liaw, Y.-L., Meinck, S., Wild, J., Christensen, J., Hughes, C., Leino, K., Rozman, M., & Cortés, D. (2021). Changes in Digital Learning During a Pandemic: Findings From the ICILS Teacher Panel. International Association for the Evaluation of Educational Achievement.
  • Gómez-Trigueros, I. M., & Yáñez de Aldecoa, C. (2021). The Digital Gender Gap in Teacher Education: The TPACK Framework for the 21st Century. European Journal of Investigation in Health, Psychology and Education, 11(4), 1333-1349. https://doi.org/10.3390/ejihpe11040097 Hall, G. E. (1974). The concerns-based adoption model: A developmental conceptualization of the adoption process within educational institutions. The Research and Development Center for Teacher Education, the University of Texas at Austin. Retriewed from: https://files.eric.ed.gov/fulltext/ED111791.pdf
  • Hatlevik, O. E. (2017). Examining the relationship between teachers’ self-efficacy, their digital competence, strategies to evaluate information and use of ICT at school. Scandinavian Journal of Educational Research, 61(5), 555-567.
  • Ibieta, A., Hinostroza, J. E., Labbé, C., & Claro, M. (2017). The role of the Internet in teachers’ professional practice: activities and factors associated with teacher use of ICT inside and outside the classroom.
  • Technology, Pedagogy and Education, 26(4), 425-438. https://doi.org/10.1080/1475939X.2017.1296489 ISTE. (2017). ISTE standards for educators. International Society for Technology in Education. Retrieved from https://www.iste.org/standards/for-educators
  • Kafyulilo, A., Fisser, P., & Voogt, J. (2016). Factors affecting teachers’ continuation of technology use in teaching. Education and Information Technologies, 21, 1535-1554. https://doi.org/10.1007/s10639-015-9398-0
  • Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R News, 2(3), 18–22. https://CRAN.R-project.org/package=randomForest
  • Liu, Y., Wang, Y., & Zhang, J. (2012). New machine learning algorithm: Random forest. In B. Liu, M. Ma, & J. Chang (Eds.), Information computing and applications: ICICA 2012 (Lecture notes in computer science, Vol. 7473, pp. 246–252). Berlin, Germany: Springer. https://doi.org/10.1007/978-3-642-34062-8_32
  • Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., & Leisch, F. (2019). e1071: Misc functions of the Department of Statistics, Probability Theory Group (Version 1.7-3) [Computer software]. The Comprehensive R Archive Network (CRAN). https://CRAN.R-project.org/package=e1071
  • Moon, T. K. (1996). The expectation-maximization algorithm. IEEE Signal Processing Magazine, 13(6), 47-60.
  • Mirzajani, H., Mahmud, R., Fauzi Mohd Ayub, A., & Wong, S. L. (2016). Teachers’ acceptance of ICT and its integration in the classroom. Quality Assurance in Education, 24(1), 26-40. https://doi.org/10.1108/QAE-06-2014-0025
  • Mumtaz, S. (2000). Factors affecting teachers' use of information and communications technology: A review of the literature. Journal of Information Technology for Teacher Education, 9(3), 319-342. https://doi.org/10.1080/14759390000200096
  • Müller, K., & Wickham, H. (2018). tibble: Simple data frames (Version 2.1.3) [Computer software]. The Comprehensive R Archive Network (CRAN). https://CRAN.R-project.org/package=tibble
  • Nikou, S. A., & Economides, A. A. (2017). Mobile-based assessment: Investigating the factors that influence behavioral intention to use. Computers & Education, 109, 56-73. https://doi.org/10.1016/j.compedu.2017.02.005
  • Noureddine, R., Boote, D., & Campbell, L. O. (2025). Assessing the validity of UTAUT among higher education instructors: A meta-analysis. Education and Information Technologies, 30, 16687–16719 (2025). https://doi.org/10.1007/s10639-025-13449-0
  • Organisation for Economic Co-operation and Development. (n.d.). Science, technology and innovation. Retrieved January 5, 2025, from https://www.oecd.org/en/topics/science-technology-and-innovation.html
  • Oliveira, T., Faria, M., Thomas, M. A., & Popovič, A. (2014). Extending the understanding of mobile banking adoption: When UTAUT meets TTF and ITM. International Journal of Information Management, 34(5), 689–703. https://doi.org/10.1016/j.ijinfomgt.2014.06.004
  • Peng, R., Abdul Razak, R., & Hajar Halili, S. (2023). Factors influencing in-service teachers’ technology integration model: Innovative strategies for educational technology. PloS One, 18(8), e0286112. https://doi.org/10.1371/journal.pone.0286112
  • Punie, Y., & Redecker, C. (2017) European Framework for the Digital Competence of Educators: DigCompEdu , Publications Office of the European Union, Luxembourg. https://doi.org/10.2760/159770
  • R Core Team. (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/
  • Revelle, W. (2020). psych: Procedures for psychological, psychometric, and personality research (Version 2.0.7) [Computer software]. The Comprehensive R Archive Network (CRAN). https://CRAN.R-project.org/package=psych
  • Rogers, E. (2003). Diffusion of innovations (5th ed.). Free Press.
  • 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. https://doi.org/10.1016/j.compedu.2018.09.009
  • Seth, M. (2020). writexl: Read and write Excel files (Version 1.3.1) [Computer software]. The Comprehensive R Archive Network (CRAN). https://CRAN.R-project.org/package=writexl
  • Shoraevna, Z., Eleupanovna, Z., Tashkenbaevna, S., Zulkarnayeva, Z., Anatolevna, L., & Nurlanbekovna, U. (2021). Teachers’ views on the use of Information and Communication Technologies (ICT) in education environments. International Journal of Emerging Technologies in Learning (iJET), 16(3), 261–273. https://doi.org/10.3991/ijet.v16i03.18801
  • Speiser, J. L., Miller, M. E., Tooze, J., & Ip, E. (2019). A comparison of random forest variable selection methods for classification prediction modeling. Expert Systems with Applications, 134, 93-101. https://doi.org/10.1016/j.eswa.2019.05.028
  • Spiteri, M., & Chang Rundgren, S. N. (2020). Literature review on the factors affecting primary teachers’ use of digital technology. Technology, Knowledge and Learning, 25(1), 115-128. https://doi.org/10.1007/s10758-018-9376-x
  • Teo, T., & Noyes, J. (2012). Explaining the intention to use technology among pre-service teachers: a multi-group analysis of the Unified Theory of Acceptance and Use of Technology. Interactive Learning Environments, 22(1), 51–66. https://doi.org/10.1080/10494820.2011.641674
  • Tierney, N., Cook, D., McBain, M., & Fay, C. (2020). naniar: Data structures, summaries, and visualizations for missing data (Version 0.6.0) [Computer software]. The Comprehensive R Archive Network (CRAN). https://CRAN.R-project.org/package=naniar
  • Tomczyk, Ł., Costas Jáuregui, V., Albuquerque de La Higuera Amato, C., Muñoz, D., Arteaga, M., Oyelere, S. S., Akyar, Ö. Y., & Porta, M. (2021). Are teachers techno-optimists or techno-pessimists? A pilot comparative study among teachers in Bolivia, Brazil, the Dominican Republic, Ecuador, Finland, Poland, Turkey, and Uruguay. Education and Information Technologies, 26, 2715–2741. https://doi.org/10.1007/s10639-020-10380-4
  • United Nations Educational, Scientific and Cultural Organization. (2018). UNESCO ICT competency framework for teachers. Paris, France: Author. Retrieved from https://unesdoc.unesco.org/ark:/48223/pf0000265721
  • United Nations Educational, Scientific and Cultural Organization. (2021). Reimagining our futures together: A new social contract for education. Paris, France: Author. Retrieved from https://unesdoc.unesco.org/ark:/48223/pf0000379707
  • U.S. Department of Education. (n.d.). National Education Technology Plan (NETP). Retrieved from https://tech.ed.gov/netp/
  • U.S. Trade and Development Agency. (2023). Uruguay - Education and training sector snapshot. Retrieved from https://www.trade.gov/country-commercial-guides/uruguay-education-and-training-sector-snapshot
  • 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.
  • Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology (UTAUT2). MIS Quarterly, 36(1), 157–178.
  • Wickham, H., & Chang, W. (2014). ggplot2: An implementation of the grammar of graphics (Version 1.0.0) [Computer software]. http://ggplot2.org
  • Wickham, H., et al. (2019). tidyverse: Easily install and load the 'Tidyverse' (Version 1.3.0) [Computer software]. https://cran.r-project.org/web/packages/tidyverse/tidyverse.pdf
  • Wickham, H., & Bryan, J. (2019). readxl: Read Excel files (Version 1.3.1) [Computer software]. https://cran.r-project.org/web/packages/readxl/readxl.pdf
  • Wickham, H., & Henry, L. (2020). tidyr: Tidy messy data (Version 1.1.2) [Computer software]. https://cran.r-project.org/web/packages/tidyr/tidyr.pdf
  • Wu, D., Zhou, C., Meng, C., & Chen, M. (2020, July). Identifying multilevel factors influencing ICT self-efficacy of K-12 teachers in China. In International Conference on Blended Learning (pp. 303–314). Cham: Springer International Publishing.
  • Xue, L., Rashid, A. M., & Ouyang, S. (2024). The Unified Theory of Acceptance and Use of Technology (UTAUT) in higher education: A systematic review. SAGE Open, 14(1). 1-22. https://doi.org/10.1177/21582440241229570
Toplam 57 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Hizmetiçi Eğitim, Eğitim Teknolojisi ve Bilgi İşlem, Öğrenme Analitiği, Öğretmen ve Öğrenci Refahı
Bölüm Makaleler
Yazarlar

Kaan Batı 0000-0002-6169-7871

Şeyma Irmak 0000-0003-3831-8244

Erken Görünüm Tarihi 15 Ağustos 2025
Yayımlanma Tarihi
Gönderilme Tarihi 27 Ocak 2025
Kabul Tarihi 11 Ağustos 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 12 Sayı: 2

Kaynak Göster

APA Batı, K., & Irmak, Ş. (2025). Hybrid Regression Analysis of Predictors of Teachers’ ICT Use in Teaching Practices: Evidence from ICILS. E-Kafkas Journal of Educational Research, 12(2), 412-427.
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