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An examination of the effect of technology-based STEM education training in the framework of technology acceptance model

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Abstract

The aim of this study is threefold: (1) to present a valid and reliable scale in the framework of the Technology Acceptance Model; (2) to reveal factors affecting pre-service science teachers’ intentions to use technology-based STEM; (3) to examine the effect of technology-based STEM education training on pre-service science teachers’ perceived ease of use, perceived usefulness, attitude, and intention. This study has two sections. Study 1 defined the reliability and validity of the Technology Based-STEM Intention Scale (TB-STEMIS) in the framework of the Technology Acceptance Model (TAM) with pre-service science teachers in Turkey. Study 2 examined the pre-service science teachers’ intentions to use technology-based STEM and the impact of technology-based STEM education training on pre-service science teachers’ intentions concerning the TAM model. The results of the study revealed that the proposed model tested after STEM training is superior to the before STEM training. Findings also indicated that technology-based STEM education training had a positive effect on pre-service science teachers’ perceived ease of use, perceived usefulness, attitude, and intention to use the technology-based STEM education. Finally, implications were discussed and recommendations were found for further studies in line with the limitations.

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The datasets are available from the corresponding author on reasonable request.

References

  • Adams, A. E., Miller, B. G., Saul, M., & Pegg, J. (2014). Supporting elementary preservice teachers to teach STEM through place-based teaching and learning experiences. Electronic Journal of Science Education, 18(5), 1–22.

    Google Scholar 

  • Ajzen, I. (2002). Constructing a TPB questionnaire: Conceptual and methodological considerations. University of Massachusetts, Amherst. Retrieved from http://www-unix.oit.umass.edu/~aizen/pdf7tpb.measurement.pdf. Accessed 12 Jan 2022

  • Ajzen, I. (2006). Constructing a theory of planned behavior questionnaire. https://people.umass.edu/~aizen/pdf/tpb.measurement.pdf

  • Akçayır, M., Akçayır, G., Pektaş, H. M., & Ocak, M. A. (2016). Augmented reality in science laboratories: The effects of augmented reality on university students’ laboratory skills and attitudes toward science laboratories. Computers in Human Behavior, 57, 334–342.

    Google Scholar 

  • Aktaş, C., & Çaycı, B. (2013). QR Kodun Mobil Eğitimde Yeni Eğitim Yöntemlerinin Geliştirilmesine Katkısı. Global Media Journal, 4(7), 1–19.

    Google Scholar 

  • Alan, B., Zengin, F. K., & Kececi, G. (2021). Effects of science, technology, engineering, and mathematics education using Algodoo to prospective science teachers’ scientific process and education orientation skills. Journal of Education, 0(0), 1–15.

    Google Scholar 

  • Al-Hariri, M. T., & Al-Hattami, A. A. (2017). Impact of students’ use of technology on their learning achievements in physiology courses at the University of Dammam. Journal of Taibah University Medical Sciences, 12(1), 82–85.

    Google Scholar 

  • Almaiah, M. A., Jalil, M. A., & Man, M. (2016). Extending the TAM to examine the effects of quality features on mobile learning acceptance. Journal of Computers in Education, 3(4), 453–485.

    Google Scholar 

  • Al-Rahmi, A. M., Al-Rahmi, W. M., Alturki, U., et al. (2022). Acceptance of mobile technologies and M-learning by university students: An empirical investigation in higher education. Education and Information Technologies, 27, 7805–7826. https://doi.org/10.1007/s10639-022-10934-8

    Article  Google Scholar 

  • American Psychological Association (2017). Ethical principles of psychologists and code of conduct, retrieved from https://www.apa.org/ethics/code/ethics-code-2017.pdf. on 27.09.2022.

  • Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A Review and recommended two-step approach. Psychological Bulletin, 103, 411–423.

    Google Scholar 

  • Ateş, H., & Garzón, J. (2022a). An integrated model for examining teachers’ intentions to use augmented reality in science courses, Education and Information Technologies. Advance online publication.

  • Ateş, H., & Garzón, J. (2022b). Drivers of teachers’ intentions to use mobile applications to teach science. Education and Information Technologies, 27(2), 2521–2542.

    Google Scholar 

  • Aydogan Yenmez, A., Gökce, S., Aydede, M. N., & Çelik, T. (2021). Investigation of pre-service teachers’ awareness of STEM and STEM teaching intention. International Online Journal of Education and Teaching, 8(1), 250–260.

    Google Scholar 

  • Baek, Y. G., Jong, J., & Kim, B. (2008). What makes teachers use of technology in the classroom? Exploring the factors affecting facilitation of technology with a Korean sample. Computers & Education, 50(8), 224–234.

    Google Scholar 

  • Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94.

    Google Scholar 

  • Baydas, Ö., & Goktas, Y. (2017). A model for preservice teachers’ intentions to use ICT in future lessons. Interactive Learning Environments, 25(7), 930–945.

    Google Scholar 

  • Beal, C. R., & Cohen, P. R. (2012). Teach ourselves: Technology to support problem posing in the STEM classroom. Creative Education, 3, 513–519.

    Google Scholar 

  • Bell, D. (2016). The reality of STEM education, design and technology teachers’ perceptions: A phenomenographic study. International Journal of Technology and Design Education, 26(1), 61–79.

    Google Scholar 

  • Benzer, A. I., & Yildiz, B. (2019). The effect of computer-aided 3D modeling activities on pre-service teachers’ spatial abilities and attitudes towards 3d modeling. Journal of Baltic Science Education, 18(3), 335–348.

    Google Scholar 

  • Buchanan, T., Sainter, P., & Saunders, G. (2013). Factors affecting faculty use of learning technologies: Implications for models of technology adoption. Journal of Computing in Higher Education, 25(1), 1–11.

    Google Scholar 

  • Bybee, R. W. (1997). Achieving scientific literacy: from purposes to practices. Heinemann.

  • Cengiz, E., & Bakırtaş, H. (2020). Technology acceptance model 3 in understanding employee's cloud computing technology. Global Business Review. Advance online publication.

  • Chang, Y. S., Chien, Y. H., Lin, H. C., Chen, M. Y., ve Hsieh, H. H. (2016). Effects of 3D CAD applications on the design creativity of students with different representational abilities. Computers in Human Behavior, 65, 107–113.

  • Chauhan, S. (2017). A meta-analysis of the impact of technology on learning effectiveness of elementary students. Computers & Education, 105, 14–30.

    Google Scholar 

  • Cheng, K. H., & Tsai, C. C. (2013). Affordances of augmented reality in science learning: Suggestions for future research. Journal of Science Education and Technology, 22(4), 449–462.

    Google Scholar 

  • Creswell, J. W. (2012). Educational research: Planning, conducting, and evaluating quantitative and qualitative research (4th ed.). Pearson.

    Google Scholar 

  • Criollo-C, S., Luján-Mora, S. and Jaramillo-Alcázar, A., 2018. Advantages and Disadvantages of M-Learning in Current Education. In 2018 IEEE World Engineering Education Conference (EDUNINE) (pp. 1–6). IEEE.

  • D’Angelo, C., Rutstein, D., Harris, C., Bernard, R., Borokhovski, E., & Haertel, G. (2014). Simulations for STEM learning: Systematic review and meta-analysis. SRI International.

    Google Scholar 

  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.

    Google Scholar 

  • Davis, F., Bagozzi, R., & Warshaw, P. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003.

    Google Scholar 

  • 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]. Retrieved from https://dspace.mit.edu/handle/1721.1/15192. Accessed 20 Sept 2021

  • Dawson, V. (2008). Use of information and communication technology by early career science teachers in Western Australia. International Journal of Science Education, 30(2), 203–219.

    Google Scholar 

  • Do, D. H., Lakhal, S., Bernier, M., Bisson, J., Bergeron, L., & St-Onge, C. (2022). Drivers of iPad use by undergraduate medical students: The technology acceptance model perspective. BMC Medical Education, 22(1), 1–12.

    Google Scholar 

  • Edmunds, R., Thorpe, M., & Conole, G. (2012). Student attitudes towards and use of ICT in course study, work and social activity: A technology acceptance model approach. British Journal of Educational Technology, 43(1), 71–84.

    Google Scholar 

  • 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.

    Google Scholar 

  • Ertmer, P. A., & Ottenbreit-Leftwich, A. (2012). Removing obstacles to the pedagogical changes required by Jonassen’s vision of authentic technology-enabled learning. Computers & Education, 64, 175–182.

    Google Scholar 

  • Esfandiar, K., Sharifi-Tehrani, M., Pratt, S., & Altinay, L. (2019). Understanding entrepreneurial intentions: A developed integrated structural model approach. Journal of Business Research, 94, 172–182.

    Google Scholar 

  • Esfandiar, K., Dowling, R., Pearce, J., & Goh, E. (2020). Personal norms and the adoption of pro-environmental binning behaviour in national parks: An integrated structural model approach. Journal of Sustainable Tourism, 28(1), 10–32.

    Google Scholar 

  • Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley.

    Google Scholar 

  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

    Google Scholar 

  • Gurer, M. D. (2021). Examining technology acceptance of pre-service mathematics teachers in Turkey: A structural equation modeling approach. Education and Information Technologies, 26(4), 4709–4729.

    Google Scholar 

  • Guzey, S. S., Harwell, M., & Moore, T. (2014). Development of an instrument to assess attitudes toward science, technology, engineering, and mathematics (STEM). School Science and Mathematics., 114(6), 271–279.

    Google Scholar 

  • Hair, J., Matthews, L., Matthews, R., & Sarstedt, M. (2017). PLS-SEM or CB-SEM: Updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107–123.

    Google Scholar 

  • Hobbs, L., Clark, J. C., & Plant, B. (2018). Successful students–STEM program: Teacher learning through a multifaceted vision for STEM education. In R. Jorgensen & R. Larkin (Eds.), STEM education in the junior secondary (pp. 133–168). Springer.

    Google Scholar 

  • Hsu, H. T., & Lin, C. C. (2022). Extending the technology acceptance model of college learners’ mobile-assisted language learning by incorporating psychological constructs. British Journal of Educational Technology, 53(2), 286–306.

    Google Scholar 

  • Huang, H. M., & Liaw, S. S. (2005). Exploring users’ attitudes and intentions toward the Web as a survey tool. Computers in Human Behavior, 21(5), 729–743.

    Google Scholar 

  • Ibáñez, M. B., & Delgado-Kloos, C. (2018). Augmented reality for STEM learning: a systematic review. Computers & Education, 123, 109–123.

    Google Scholar 

  • Ibili, E., Resnyansky, D., & Billinghurst, M. (2019). Applying the technology acceptance model to understand maths teachers’ perceptions towards an augmented reality tutoring system. Education and Information Technologies, 24(5), 2653–2675.

    Google Scholar 

  • Incedayi, N. (2018). The Impact of Using Multimedia Technologies on Students Academic Achievement. International Journal of Humanities Social Sciences and Education, 5(1), 40–47.

    Google Scholar 

  • Jang, J., Ko, Y., Shin, W. S., & Han, I. (2021). Augmented reality and virtual reality for learning: An examination using an extended technology acceptance model. IEEE Access, 9, 6798–6809.

    Google Scholar 

  • Joo, Y. J., Park, S., & Lim, E. (2018). Factors influencing preservice teachers’ intention to use technology: TPACK, teacher self-efficacy, and technology acceptance model. Educational Technology & Society, 21(3), 48–59.

    Google Scholar 

  • Karisan, D., Macalalag, A., & Johnson, J. (2019). The effect of methods course on preservice teachers’ awareness and intentions of teaching science, technology, engineering, and mathematics (STEM) subjects. International Journal of Research in Education and Science, 5(1), 22–35.

    Google Scholar 

  • Kimiagari, S., & Baei, F. (2021). Promoting e-banking actual usage: mix of technology acceptance model and technology-organisation-environment framework. Enterprise Information Systems, Advance online publication.

  • Kirschner, P., & Selinger, M. (2003). The state of affairs of teacher education with respect to information and communications technology. Technology, Pedagogy and Education, 12(1), 5–18.

    Google Scholar 

  • Kline, R. B. (2015). Principles and practice of structural equation modeling (4th ed.). Guilford Press.

    MATH  Google Scholar 

  • Kong, S. C. (2018). Parents’ perceptions of e-learning in school education: Implications for the partnership between schools and parents. Technology, Pedagogy and Education, 27(1), 15–31.

    MathSciNet  Google Scholar 

  • Lai, H. C., Chang, C. Y., Wen-Shiane, L., Fan, Y. L., & Wu, Y. T. (2013). The implementation of mobile learning in outdoor education: Application of QR codes. British Journal of Educational Technology, 44(2), 57–62.

    Google Scholar 

  • Law, C. Y., & So, S. (2010). QR codes in education. Journal of Educational Technology Development and Exchange, 3(1), 1–7.

    Google Scholar 

  • Lee, Y., Kozar, K. A., & Larsen, K. (2003). The technology acceptance model: Past, present, and future. Communications of the Association for Information Systems, 12(50), 752–780.

    Google Scholar 

  • Liao, K. H. (2017). The abilities of understanding spatial relations, spatial orientation, and spatial visualization affect 3D product design performance: using carton box design as an example. International Journal of Technology and Design Education, 27(1), 131–147.

  • Lin, K. Y., & Williams, P. J. (2016). Taiwanese preservice teachers’ science, technology, engineering, and mathematics teaching intention. International Journal of Science and Mathematics Education, 14(6), 1021–1036.

    Google Scholar 

  • Luh, D. B., & Chen, S. N. (2013). A novel CAI system for space conceptualization training in perspective sketching. International Journal of Technology and Design Education, 23(1), 147–160.

    Google Scholar 

  • Menabò, L., Sansavini, A., Brighi, A., Skrzypiec, G., & Guarini, A. (2021). Promoting the integration of technology in teaching: An analysis of the factors that increase the intention to use technologies among Italian teachers. Journal of Computer Assisted Learning, 37(6), 1566–1577.

    Google Scholar 

  • Miller, J. (2019). STEM Education in the primary years to support mathematical thinking: Using coding to identify mathematical structures and patterns. ZDM, 51(6), 915–927.

    Google Scholar 

  • Ministry of National Education (MoNE). (2018). Science curriculum (primary and middle school 3, 4, 5, 6, 7 and 8th grades).

  • Mutambara, D., & Bayaga, A. (2020). Predicting rural STEM teachers’ acceptance of mobile learning in the fourth industrial revolution. Journal of Construction Project Management and Innovation, 10(2), 14–29.

    Google Scholar 

  • National Academies of Sciences, Engineering, Medicine (NASEM). (2019). Integrating Social Care Into the Delivery of Health Care: Moving Upstream to Improve the Nation’s Health. National Academies Press.

    Google Scholar 

  • National Research Council. (2011). Learning science through computer games and simulations. Committee on Science Learning: Computer Games, Simulations, and Education. In M. A. Honey & M. L. Hilton (Eds.), Board on Science Education, Division of Behavioral and Social Sciences and Education. The National Academies Press.

    Google Scholar 

  • Nelson, M. J., & Hawk, N. A. (2020). The impact of field experiences on prospective preservice teachers’ technology integration beliefs and intentions. Teaching and Teacher Education, 89, 1–12.

    Google Scholar 

  • Ng, O. L., & Park, M. (2021). Using an enhanced video-engagement innovation to support STEM teachers’ professional development in technology-based instruction. Educational Technology & Society, 24(4), 193–204.

    Google Scholar 

  • Nikou, S. A., & Economides, A. A. (2017). Mobile-based assessment: Integrating acceptance and motivational factors into a combined model of self-determination theory and technology acceptance. Computers in Human Behavior, 68, 83–95.

    Google Scholar 

  • Papakostas, C., Troussas, C., Krouska, A., & Sgouropoulou, C. (2022). Exploring users’ behavioral intention to adopt mobile augmented reality in education through an extended technology acceptance model. International Journal of Human–Computer Interaction. Advance online publication.

  • Parikh, A., Patel, J. D., & Jaiswal, A. K. (2021). Managing job applications online: Integrating website informativeness and compatibility in theory of planned behaviour and technology acceptance model. Decision, 48(1), 97–113.

    Google Scholar 

  • Penner, D. E., Giles, N. D., Lehrer, R., & Schauble, L. (1997). Building functional models: Designing an elbow. Journal of Research in Science Teaching, 34, 125–143.

    Google Scholar 

  • Prinsley, R., & Johnston, E. (2015). Transforming STEM teaching in Australian primary schools: Everybody’s business. Australian Government. Office of the Chief Scientist. Position Paper.

  • Roehrig, G. H., Moore, T. J., Wang, H.-H., & Park, M. S. (2012). Is adding the E enough? Investigating the impact of K-12 engineering standards on the implementation of STEM integration. School Science and Mathematics, 112(1), 31–44.

    Google Scholar 

  • Sagheer, N., Khan, K., Fahd, S., Mahmood, S., Rashid, T., & Jamil, H. (2022). Factors affecting adaptability of cryptocurrency: An application of technology acceptance model. Frontiers in Psychology, 13, 1–12.

    Google Scholar 

  • Scherer, R., & Teo, T. (2019). Unpacking teachers’ intentions to integrate technology: A meta-analysis. Educational Research Review, 27, 90–109.

    Google Scholar 

  • Shin, J., Moon, S., Cho, B. H., Hwang, S., & Choi, B. (2022). Extended technology acceptance model to explain the mechanism of modular construction adoption. Journal of Cleaner Production, 342, 1–18.

    Google Scholar 

  • Sırakaya, M., & Sırakaya, D. A. (2020). Augmented reality in STEM education: A systematic review. Interactive Learning Environment, 142, 1–14.

    Google Scholar 

  • 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), 1–12.

    Google Scholar 

  • Spallone, R. (2015). Digital reconstruction of demolished architectural masterpieces, 3D modeling, and animation: The case study of Turin Horse Racing by Mollino. In S. Brusaporci (Ed.), Handbook of research on emerging digital tools for architectural surveying, modeling, and representation (pp. 476–509). IGI Global.

    Google Scholar 

  • Stegmann, K. (2020). Effects of digital learning on the acquisition of knowledge and skills at school. Zeitschrift Für Padagogik, 2, 174–190.

    Google Scholar 

  • Sungur Gül, K. & Saylan Kırmızıgül, A. (2022). Algodoo based STEM education: A case study of pre-service science teachers. Education and Information Technologies. Advance online publication.

  • Sungur-Gül, K., & Ateş, H. (2021). Understanding pre-service teachers’ mobile learning readiness using theory of planned behavior. Educational Technology & Society, 24(2), 44–57.

    Google Scholar 

  • Tamim, R. M., Bernard, R. M., Borokhovski, E., Abrami, P. C., & Schmid, R. F. (2011). What forty years of research says about the impact of technology on learning: A second- order meta-analysis and validation study. Review of Educational Research, 81(1), 4–28.

    Google Scholar 

  • Teo, T., & Milutinovic, V. (2015). Modelling the intention to use technology for teaching mathematics among pre-service teachers in Serbia. Australasian Journal of Educational Technology, 31(4), 363–380.

    Google Scholar 

  • Teo, T., & Noyes, J. (2014). 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.

    Google Scholar 

  • Teo, T., Ursavas, O. F., & Bahcekapili, E. (2012). An assessment of preservice teachers’ technology acceptance in Turkey: A structural equation modeling approach. The Asia-Pacific Education Researcher, 21(1), 191–202.

    Google Scholar 

  • 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.

    Google Scholar 

  • Teo, T., Milutinović, V., Zhou, M., & Banković, D. (2017). Traditional vs. innovative uses of computers among mathematics pre-service teachers in Serbia. Interactive Learning Environments, 25(7), 811–827.

    Google Scholar 

  • The World Medical Association (2018). WMA declaration of Helsinki – Ethical principles for medical research involving human subjects, retrieved from https://www.wma.net/policies-post/wma-declaration-of-helsinki-ethical-principles-for-medical-research-involving-human-subjects/#:~:text=Medical%20research%20involving%20human%20subjects%20must%20conform%20to%20generally%20accepted,%2C%20as%20appropriate%2C%20animal%20experimentation. on 27.09.2022.

  • Turkish Council of Higher Education. (2018). New Teacher Training Programs: Science teaching undergraduate program. Retrieved from https://www.yok.gov.tr/Documents/Kurumsal/egitim_ogretim_dairesi/Yeni-Ogretmen-Yetistirme-Lisans-Programlari/Fen_Bilgisi_Ogretmenligi_Lisans_Programi.pdf. Accessed 03 Feb 2022

  • Unal, E., & Uzun, A. M. (2021). Understanding university students’ behavioral intention to use Edmodo through the lens of an extended technology acceptance model. British Journal of Educational Technology, 52(2), 619–637.

    Google Scholar 

  • Ünlü, Z. K., & Dökme, İ. (2022) A systematic review of 5E model in science education: proposing a skill-based STEM instructional model within the 21-st century skills. International Journal of Science Education, Advance online publication.

  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 425–478.

  • Vlasopoulou, M., Kalogiannakis, M., & Sifaki, E. (2021). Investigating teachers' attitudes and behavioral intentions for the impending integration of STEM education in primary schools. In Handbook of Research on Using Educational Robotics to Facilitate Student Learning (pp. 235–256). IGI Global.

  • Wang, Y., Yu, L., & Yu, Z. (2022). An extended CCtalk technology acceptance model in EFL education. Education and Information Technologies, 27, 6621–6640.

    Google Scholar 

  • Watson, J. H., & Rockinson-Szapkiw, A. (2021). Predicting preservice teachers’ intention to use technology-enabled learning. Computers & Education, 168, 1–10.

    Google Scholar 

  • Wu, Y., & Anderson, O. R. (2015). Technology-enhanced STEM (science, technology, engineering, and mathematics) education. Journal of Computers in Education, 2(3), 245–249.

    Google Scholar 

  • Wu, H.-K., Lee, S.W.-Y., Chang, H.-Y., & Liang, J.-C. (2013). Current status, opportunities and challenges of augmented reality in education. Computers & Education, 62, 41–49.

    Google Scholar 

  • Yang, D., & Baldwin, S. J. (2020). Using technology to support student learning in an integrated STEM learning environment. International Journal of Technology in Education and Science, 4(1), 1–11.

    Google Scholar 

  • Yeo, S., Rutherford, T., & Campbell, T. (2022). Understanding elementary mathematics teachers’ intention to use a digital game through the technology acceptance model. Education and Information Technologies, 27, 11515–11536.

    Google Scholar 

  • Yeop, M. A., Yaakob, M. F. M., Wong, K. T., Don, Y., & Zain, F. M. (2019). Implementation of ICT policy (blended learning approach): Investigating factors of behavioural intention and use Behaviour. International Journal of Instruction, 12(2), 767–782.

    Google Scholar 

  • Yuen, A. H. K., & Ma, W. W. K. (2008). Exploring teacher acceptance of e-learning technology. Asia-Pacific Journal of Teacher Education, 36(3), 229–243.

    Google Scholar 

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Sungur Gül, K., Ateş, H. An examination of the effect of technology-based STEM education training in the framework of technology acceptance model. Educ Inf Technol 28, 8761–8787 (2023). https://doi.org/10.1007/s10639-022-11539-x

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