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Using the technology acceptance model to assess how preservice teachers’ view educational technology in middle and high school classrooms

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Abstract

Elementary, middle, and high school students need opportunities to engage with educational technology. This is particularly essential for those students who may not have access to new technologies at home and/or school. The socioeconomic status continues to increase the digital divide and equity in education in terms of access to technology; and as new and advanced technology becomes more available for some, others are falling further behind. A key component in ensuring all K-12 students receive opportunities to engage with technology is to prepare preservice teachers to be proficient at using new educational technologies in their classrooms. Virtual Reality (VR) environments are gaining traction across some environments. However, in the United States, some states limit or exclude VR from elementary and middle grades. In a recent service-learning project, 14 preservice teachers were introduced to two types of floor-robots and one style of a VR headset. Most preservice teachers had not used any of the technology prior to the required course. After learning how to manipulate floor-robots and navigate virtual environments, they taught middle and high school students with exceptionalities how to use technology to enhance comprehension of mathematics and social studies content. Of the 14 preservice teachers in the course, seven agreed to allow their surveys and reflections to be used for data collection and analysis. Results demonstrate that preservice teachers view technology for use in school settings favorably, and they believed that middle and high school students with exceptionalities benefitted from the educational technology instructional experiences. However, there were some concerns about costs.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  • Ajzen, I., & Fishbein, M. (1977). Attitude-behavior relations: A theoretical analysis and review of empirical research. Psychological Bulletin, 84(5), 888–918.

    Article  Google Scholar 

  • Alenazy, W. M., Al-Rahmi, W. M., & Khan, M. S. (2019). Validation of TAM model on social media use for collaborative learning to enhance collaborative authoring. Institute of Electrical and Electronics Engineers, 7, 71550–71562.

    Google Scholar 

  • Casey, J. E., Gill, P., Pennington, L. & Mireles, S. V. (2018). Lines, roamers, and squares: Oh my! Enhancing Hispanic students’ understanding and use of programming. Education and Information Technologies, 23(4), 1531–1546. https://doi.org/10.1007/s10639-017-9677-z

  • Casey, J. E., Pennington, L., & Mireles, S. V. (2020). Technology acceptance model: assessing preservice teachers’ acceptance of floor-robots as a useful pedagogical tool. Technology, Knowledge and Learning, 26, 499–514. https://doi.org/10.1007/s10758-020-09452-8

  • Baby, A., & Kannammal, A. (2020). Network path analysis for developing an enhanced TAM model: A user-centric e-learning perspective. Computers in Human Behavior, 107, 106081. https://doi.org/10.1016/j.chb.2019.07.024

    Article  Google Scholar 

  • Bahaj, S. A., O., Aljaaidib, K. S., & Ahmeda, T. M. (2019). Using TAM model to empirically examine students’ attitudes towards e-services in college of business administration. Management Science Letters, 9(5), 651–660.

  • Bennett, S., & Matton, K. (2010). Beyond the ‘digital natives’ debate: Towards a more nuanced understanding of students’ technology experiences. Journal of Computer Assisted Learning, 26(5):321–333. https://doi.org/10.1111/j.1365-2729.2010.00360.x

  • Bicer, A., & Capraro, R. M. (2017). Longitudinal effects of technology integration and teacher professional development on students’ mathematics achievement. EURASIA Journal of Mathematics Science and Technology Education, 13(3), 815–833. https://doi.org/10.12973/eurasia.2017.00645a

    Article  Google Scholar 

  • Brasiel, S., Jeong, S., Ames, C., Lawanto, K., & Yuan, M. (2016). Effects of educational technology on mathematics achievement for K-12 students in Utah. Journal of Online Learning Research, 2(3), 205–226.

    Google Scholar 

  • Chandra, S., Chang, A., Day, L., Fazlullah, A., Liu, J., McBride, L., Mudalige, T, Weiss, D., & Fazlullah, A. (2020). Closing the K–12 digital divide in the age of distance learning. Common Sense and Boston Consulting Group: Boston, MA, USA.

  • Cheng, L., Antonenko, P. D., Ritzhaupt, A. D., Dawson, K., Miller, D., MacFadden, B. J., Grant, C., Sheppard, T. D., & Ziegler, M. (2020). Exploring the influence of teachers’ beliefs and 3D printing integrated STEM instruction on students’ STEM motivation. Computers & Education, 158, 1–18. https://doi.org/10.1016/j.compedu.2020.103983

    Article  Google Scholar 

  • Cheung, A. C. K., & Slavin, R. (2013). The effectiveness of educational technology applications for enhancing mathematics achievement in K-12 classrooms: A meta-analysis. Educational Research Review, 9, 88–113.

    Article  Google Scholar 

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

  • Davis, F., & Venkatesh, V. (1996). A critical assessment of potential measurement biases in the technology acceptance model: Three experiments. International Journal of Human-Computer Studies, 45(1), 19–45. https://doi.org/10.1006/ijhc.1996.0040

    Article  Google Scholar 

  • Denzin, N. (1970). The research act: A theoretical introduction to sociological methods. Aldine.

    Google Scholar 

  • Dolan, J. E. (2016). Splicing the Divide: A review of research on the evolving digital divide among K–12 Students. Journal of Research on Technology in Education, 48(1), 16–37. https://doi.org/10.1080/15391523.2015.1103147

    Article  Google Scholar 

  • Ertmer, P. A., & Ottenbreit-Leftwich, A. (2013). Removing obstacles to the pedagogical changes required by Jonassen’s vision of authentic technology-enabled learning. Computers & Education, 64, 175–182. https://doi.org/10.1016/j.compedu.2012.10.008

    Article  Google Scholar 

  • Fernández-Batanero, J., Román-Graván, P., Reyes-Rebollo, M., & Montenegro-Rueda, M. (2021). Impact of educational technology on teacher stress and anxiety: A literature review. International Journal of Environmental Research and Public Health, 18(2), 528. https://doi.org/10.3390/ijerph18020548

    Article  Google Scholar 

  • Fleiss, J. L. (1971). Measuring nominal scale agreement among many raters. Psychological Bulletin, 76(5), 378–382.

    Article  Google Scholar 

  • Han, S., Capraro, R., & Capraro, M. (2014). How science, technology, engineering, and mathematics (STEM) project-based learning )PBL) affects high, middle, and low achievers differently: The impact of students factors on achievement. International Journal of Science and Mathematics Education, 13, 1089–1113. https://doi.org/10.1007/s10763-014-9526-0

    Article  Google Scholar 

  • Hargittai, E. (2002). Second-level digital divide: Differences in people's online skills. First Monday, 7(4). http://firstmonday.org/issues/issue7_4/hargittai/index.html. Accessed January 2022

  • Herro, D., & Quigley, C. (2017). Exploring teachers’ perceptions of STEAM teaching through professional development: Implications for teacher educators. Professional Development in Education, 43(3), 416–438. https://doi.org/10.1080/19415257.2016.1205507

    Article  Google Scholar 

  • Hohlfeld, T. A., Ritzhaupt, A. D., Dawson, K., & Wilson, M. L. (2017). An examination of seven years of technology integration in Florida schools: Through the lens of the Levels of Digital Divide in Schools. Computers & Education, 113, 135–161. https://doi.org/10.1016/j.compedu.2017.05.017

    Article  Google Scholar 

  • Joo, Y. J., Park, S., & Lim, E. (2017). 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 

  • Judd, T. (2018). The rise and fall (?) of the digital natives. Australasian Journal of Educational Technology, 34(5), 99–119.

    Article  Google Scholar 

  • Kent, A. M., & Giles, R. M. (2017). Preservice teachers’ technology self-efficacy. Southeastern Regional Association of Teacher Educators Journal, 26(1), 9–20.

    Google Scholar 

  • Landis, J. R., & Koch, G. G. (1977) The measurement of observer agreement for categorical data. Biometrics, 33, 159–174.

  • Landon-Hays, M., Peterson-Ahmad, M. B., & Frazier, A. D. (2020). Learning to teach: How a simulated learning environment can connect theory to practice in general and special education educator preparation programs. Education Sciences, 10(184). https://doi.org/10.3390/educsci10070184

  • Liao, Y.-C., Ottenbreit-Leftwich, A., Karlin, M., Glazewski, K., & Brush, T. (2017). Supporting change in teacher practice: Examining shifts of teachers’ professional development preferences and needs for technology integration. Contemporary Issues in Technology and Teacher Education, 17(4), 522–548.

    Google Scholar 

  • Mailizar, M., Almanthari, A., & Maulina, S. (2021). Examining teachers’ behavioral intention to use e-learning in teaching of mathematics: An extended TAM model. Contemporary Educational Technology, 13(2), ep298. https://doi.org/10.30935/cedtech/9709

    Article  Google Scholar 

  • Muhammad, N. M., & Schneider, M. (2021). The role of EdTech in enhancing learners’ motivation. Selangor Science &Technology Review, Special Issue: Science and Technology for Society, 5(5), 73–77.

  • Muhammad, N. M., Schneider, M., Hill, A., & Yau, D. M. (2019). The negative impacts of EdTech: EQ perspectives. In Society for Information Technology & Teacher Education International Conference (np).

  • Pierce, G. L., & Cleary, P. F. (2016). The K-12 educational technology value chain: Apps for kids, tools for teachers and levers for reform. Education and Information Technologies, 21, 863–880. https://doi.org/10.1007/s10639-014-9357-1

    Article  Google Scholar 

  • Spiteri, M., & Chang Rundgren, S. N. (2020). Literature review on the factors affecting primary teachers’ use of digital technology. Technology, Knowledge, & Learning, 25, 115–128. https://doi.org/10.1007/s10758-018-9376-x

  • Stošić, L. (2015). The importance of educational technology in teaching. International Journal of Cognitive Research in Science, Engineering and Education, 3(1), 111–114.

    Article  Google Scholar 

  • van Deursen, A. J. A. M., & Helsper, E. J. (2015). A nuanced understanding of Internet use and non-use among the elderly. European Journal of Communication, 30(2), 171–187.

    Article  Google Scholar 

  • van Deursen, A. J. A. M., & Mossberger, K. (2018). Any thing for anyone? A new digital divide in internet-of-things skills. Policy and Internet, 10, 122–140. https://doi.org/10.1002/poi3.171

    Article  Google Scholar 

  • van Deursen, A. J. A. M., & van Dijk, J. A. G. M. (2019). The first-level digital divide shifts from inequalities in physical access to inequalities in material access. New Media & Society, 21(2), 354–375. https://doi.org/10.1177/1461444818797082

    Article  Google Scholar 

  • Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451–481.

    Article  Google Scholar 

  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(2), 425–478.

    Article  Google Scholar 

  • Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2). https://doi.org/10.1287/mnsc.46.2.186.11926

  • Warschauer, M., Knobel, M., & Stone, L. (2004). Technology and equity in schooling: Deconstructing the digital divide. Educational Policy, 18(4), 562–588. https://doi.org/10.1177/0895904804266469

    Article  Google Scholar 

  • 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, 1–16. https://doi.org/10.1016/j.compedu.2020.103941

    Article  Google Scholar 

  • Wu, B., Yu, X., & Gu, X. (2020). Effectiveness of immersive virtual reality using head-mounted displays on learning performance: A meta-analysis. British Journal of Educational Technology, 51(6), 1991–2005. https://doi.org/10.1111/bjet.13023

    Article  Google Scholar 

  • Yin, R. K. (2014). Case study research: Design and methods (5th ed.). SAGE Publications.

    Google Scholar 

  • Yurtseven Avci, Z., O’Dwyer, L. M., & Lawson, J. (2020). Designing effective professional development for technology integration in schools. Journal of Computer Assisted Learning, 36, 160–177. https://doi.org/10.1111/jcal.12394

    Article  Google Scholar 

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Funding

The first author and principal investigator wants to acknowledge that funds from a USDA-NIFA grant were used to purchase educational technology used in this project.

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Authors and Affiliations

  1. Department of Curriculum & Instruction, Texas A&M University - Central Texas, 1001 Leadership Place WH 322J, Killeen, TX, USA

    J. Elizabeth Casey & Kimberly Kuklies

  2. Department of Counseling & Psychology, Texas A&M University - Central Texas, Killeen, TX, USA

    Jeff Kirk

  3. Faculty Affairs and Diversity, University of Texas RGV, Brownsville, TX, USA

    Selina V. Mireles

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  1. J. Elizabeth Casey
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Correspondence to J. Elizabeth Casey.

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Casey, J.E., Kirk, J., Kuklies, K. et al. Using the technology acceptance model to assess how preservice teachers’ view educational technology in middle and high school classrooms. Educ Inf Technol 28, 2361–2382 (2023). https://doi.org/10.1007/s10639-022-11263-6

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