[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

Applying the technology acceptance model to understand maths teachers’ perceptions towards an augmented reality tutoring system

  • Published:
Education and Information Technologies Aims and scope Submit manuscript

Abstract

This paper examines mathematics teachers’ level of acceptance and intention to use the Augmented Reality Geometry Tutorial System (ARGTS), a mobile Augmented Reality (AR) application developed to enhance students’ 3D geometric thinking skills. ARGTS was shared with mathematics teachers, who were then surveyed using the Technology Acceptance Model (TAM) to understand their acceptance of the technology. We also examined the external variables of Anxiety, Social Norms and Satisfaction. The effect of the teacher’s gender, degree of graduate status and number of years of teaching experience on the subscales of the TAM model were examined. We found that the Perceived Ease of Use (PEU) had a direct effect on the Perceived Usefulness (PU) in accordance with the Technology Acceptance Model (TAM). Both variables together affect Satisfaction (SF), however PEU had no direct effect on Attitude (AT). In addition, while Social Norms (SN) had a direct effect on PU and PEU, there was no direct effect on Behavioural Intention (BI). Anxiety (ANX) had a direct effect on PEU, but no effect on PU and SF. While there was a direct effect of SF on PEU, no direct effect was found on BI. We explain how the results of this study could help improve the understanding of AR acceptance by teachers and provide important guidelines for AR researchers, developers and practitioners.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. https://www.vuforia.com/

  2. https://unity3d.com/

References

  • Abdullah, F., & Ward, R. (2016). Developing a general extended technology acceptance model for E-learning (GETAMEL) by analysing commonly used external factors. Computers in Human Behavior, 56, 238–256.

    Article  Google Scholar 

  • Abdullah, F., Ward, R., & Ahmed, E. (2016). Investigating the influence of the most commonly used external variables of TAM on students’ perceived ease of use (PEOU) and perceived usefulness (PU) of e-portfolios. Computers in Human Behavior, 63, 75–90.

    Article  Google Scholar 

  • Agarwal, R., & Karahanna, E. (2000). Time flies when you're having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24, 665–694.

    Article  Google Scholar 

  • Al-Azawei, A., & Lundqvist, K. (2015). Learner differences in perceived satisfaction of an online learning: An extension to the technology acceptance model in an Arabic sample. Electronic Journal of e-Learning, 13(5), 408–426.

    Google Scholar 

  • Arvanitis, T. N., Williams, D. D., Knight, J. F., Baber, C., Gargalakos, M., Sotiriou, S., & Bogner, F. X. (2011). A human factors study of technology acceptance of a prototype mobile augmented reality system for science education. Advanced Science Letters, 4(11–12), 3342–3352.

    Article  Google Scholar 

  • Bagozzi, R. P., Gopinath, M., & Nyer, P. U. (1999). The role of emotions in marketing. Journal of the Academy of Marketing Science, 27(2), 184–206.

    Article  Google Scholar 

  • Balog, A., & Pribeanu, C. (2010). The role of perceived enjoyment in the students’ acceptance of an augmented reality teaching platform: A structural equation modelling approach. Studies in Informatics and Control, 19(3), 319–330.

    Article  Google Scholar 

  • Bellone, L. M., & Czerniak, C. M. (2001). Teachers’ beliefs about accommodating students’ learning styles in science classes. Electronic Journal of Science Education, 6(2), 4–29.

    Google Scholar 

  • Bhattacherjee, A. (2001). An empirical analysis of the antecedents of electronic commerce service continuance. Decision Support Systems, 32(2), 201–214.

    Article  Google Scholar 

  • Bonsón, E., Escobar, T., & Ratkai, M. (2014). Testing the inter-relations of factors that may support continued use intention: The case of Facebook. Social Science Information, 53(3), 293–310.

    Article  Google Scholar 

  • Briz-Ponce, L., Pereira, A., Carvalho, L., Juanes-Méndez, J. A., & García-Peñalvo, F. J. (2017). Learning with mobile technologies–students’ behavior. Computers in Human Behavior, 72, 612–620.

    Article  Google Scholar 

  • Bujak, K. R., Radu, I., Catrambone, R., Macintyre, B., Zheng, R., & Golubski, G. (2013). A psychological perspective on augmented reality in the mathematics classroom. Computers & Education, 68, 536–544.

    Article  Google Scholar 

  • Byrne, B. M. (2001). Structural equation modeling with AMOS, EQS, and LISREL: Comparative approaches to testing for the factorial validity of a measuring instrument. International Journal of Testing, 1(1), 55–86.

    Article  Google Scholar 

  • Chang, C. T., Hajiyev, J., & Su, C. R. (2017). Examining the students’ behavioral intention to use e-learning in Azerbaijan? The general extended technology acceptance model for e-learning approach. Computers & Education, 111, 128–143.

    Article  Google Scholar 

  • Cheng, M., & Yuen, A. H. K. (2018). Student continuance of learning management system use: A longitudinal exploration. Computers & Education, 120, 241–253.

    Article  Google Scholar 

  • Cheon, J., Lee, S., Crooks, S. M., & Song, J. (2012). An investigation of mobile learning readiness in higher education based on the theory of planned behavior. Computers & Education, 59(3), 1054–1064.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Drennan, J., Kennedy, J., & Pisarski, A. (2005). Factors affecting student attitudes toward flexible online learning in management education. The Journal of Educational Research, 98(6), 331–338.

    Article  Google Scholar 

  • Dunleavy, M., Dede, C., & Mitchell, R. (2009). Affordances and limitations of immersive participatory augmented reality simulations for teaching and learning. Journal of Science Education and Technology, 18(1), 7–22.

    Article  Google Scholar 

  • Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18, 382–388.

    Article  Google Scholar 

  • Gürbüz, R., & Gülburnu, M. (2013). Effect of teaching geometry with use Cabri 3D in eighth grade on conceptual learning. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 4(3), 224–241.

    Google Scholar 

  • Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (6th ed.). New York: Pearson.

  • Haugstvedt, A. C. Krogstie, J. (2017). Mobile augmented reality for cultural heritage: A technology acceptance study. In 2012 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), Georgia Tech, Atlanta, USA, 5–8 November 2012. Washington: IEEE Computer Society.

  • Huang, Y. M. (2016). The factors that predispose students to continuously use cloud services: Social and technological perspectives. Computers & Education, 97, 86–96.

    Article  Google Scholar 

  • Hunt, H. K. (1977). CS/D - Overview and future research directions. In H. K. Hunt (Ed.), Conceptualizion and measurement of consumer satisfaction and dissatisfaction (pp. 77-103). Cambridge, MA: Marketing Science Institute.

  • Ibáñez, M.B., Di Serio, Á., Villarán, D., Delgado-Kloos, C. (2016) The acceptance of learning augmented reality environments: a case study. In: 2016 IEEE 16th International Conference on Advanced Learning Technologies (ICALT), (pp. 307–311). IEEE.

  • İbili, E., Çat, M., Resnyansky, D., Şahin, S., & Billinghurst, M. (2019). An assessment of geometry teaching supported with augmented reality teaching materials to enhance students’ 3D geometry thinking skills. International Journal of Mathematical Education in Science and Technology, 1–23.

  • Igbaria, M., & Parasuraman, S. (1989). A path analytic study of individual characteristics, computer anxiety and attitudes toward microcomputers. Journal of Management, 15(3), 373–388.

    Article  Google Scholar 

  • Islam, A. N. (2011). Understanding the continued usage intention of educators toward an e-learning system. International Journal of E-Adoption (IJEA), 3(2), 54–69.

    Article  Google Scholar 

  • Joo, Y. J., Park, S., & Shin, E. K. (2017). Students' expectation, satisfaction, and continuance intention to use digital textbooks. Computers in Human Behavior, 69, 83–90.

    Article  Google Scholar 

  • Karakırık, E. (2011). Geometry teaching with Dynamic geometry and Sketchpad. In E. Karakırık (Ed.), Technology use in mathematics education (pp. 67–96). Ankara: Nobel Publishing.

    Google Scholar 

  • Kim, K., Hwang, J., Zo, H., & Lee, H. (2016). Understanding users’ continuance intention toward smartphone augmented reality applications. Information Development, 32(2), 161–174.

    Article  Google Scholar 

  • Lai, V. S., & Li, H. (2005). Technology acceptance model for internet banking: An invariance analysis. Information & Management, 42(2), 373–386.

    Article  Google Scholar 

  • Lave, J., Wenger, E., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation (Vol. 521423740). Cambridge: Cambridge university press.

    Book  Google Scholar 

  • Lee, M. C. (2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation–confirmation model. Computers & Education, 54(2), 506–516.

    Article  Google Scholar 

  • Lee, M. K., Cheung, C. M., & Chen, Z. (2005). Acceptance of internet-based learning medium: The role of extrinsic and intrinsic motivation. Information & Management, 42(8), 1095–1104.

    Article  Google Scholar 

  • Lee, G., Teo, T., Kim, S. and M. Billinghurst (2017). Mixed reality collaboration through sharing a live panorama. In SIGGRAPH Asia 2017 Mobile Graphics Interactive Applications (p. 14). New York: ACM.

  • Lin, H. F., & Chen, C. H. (2017). Combining the technology acceptance model and uses and gratifications theory to examine the usage behavior of an augmented reality tour-sharing application. Symmetry, 9(7), 113.

    Article  Google Scholar 

  • Lin, H. C., Chiu, Y. H., Chen, Y. J., Wuang, Y. P., Chen, C. P., Wang, C. C., Huang, C. L., Wu, T. M., & Ho, W. H. (2017). Continued use of an interactive computer game-based visual perception learning system in children with developmental delay. International Journal of Medical Informatics, 107, 76–87.

    Article  Google Scholar 

  • M.E.B - T. C. Milli Eğitim Bakanlığı (Republic of Turkey, Ministry of National Education), (2018) Elementary-Secondary Mathematics Lesson Curriculum (1–8. Grade), Ankara.

  • M.E.B - T. C. Milli Eğitim Bakanlığı (Republic of Turkey, Ministry of National Education), Board of Education (2009). Secondary Mathematics Lesson Curriculum (6–8 Grade), Ankara.

  • Mathieson, K. (1991). Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2(3), 173–191.

    Article  Google Scholar 

  • Mlaiki, A., Kailka, M., & Kefi, H. (2011). Facebook… encore, encore…! Rôle de l’affect, de l’habitude et de la surcharge informationnelle dans la continuité d’utilisation des réseaux sociaux numériques. Disponible sur: https://basepub.dauphine.fr/bitstream/handle/123456789/7963/Kalika_aim2011.PDF?sequence=1. Accessed 01 Apr 2019.

  • Nikou, S. A., & Economides, A. A. (2017a). Mobile-based assessment: Investigating the factors that influence behavioral intention to use. Computers & Education, 109, 56–73.

    Article  Google Scholar 

  • Nikou, S. A., & Economides, A. A. (2017b). 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.

    Article  Google Scholar 

  • Osman N. B. (2013). Extending the technology acceptance model for mobile government systems, The international Arab conference on information technology (ACIT’2013). https://pdfs.semanticscholar.org/e7f4/bd830907d2b7d86a0d0a0ea578433de80ad0.pdf. Accessed 01 Apr 2019.

  • Prieto, J. C. S., Migueláñez, S. O., & García-Peñalvo, F. J. (2017). Utilizarán los futuros docentes las tecnologías móviles? Validación de una propuesta de modelo TAM extendido. Revista de Educación a Distancia, (52).

  • Quintero, E., Salinas, P., González-Mendívil, E., & Ramírez, H. (2015). Augmented reality app for calculus: A proposal for the development of spatial visualization. Procedia Computer Science, 75, 301–305.

    Article  Google Scholar 

  • Revythi, A., & Tselios, N. (2017). Extension of technology acceptance model by using system usability scale to assess behavioral intention to use e-learning. arXiv preprint arXiv:1704.06127.

  • Roberts, P., & Henderson, R. (2000). Information technology acceptance in a sample of government employees: a test of the technology acceptance model. Interacting with Computers, 12(5), 427–443.

    Article  Google Scholar 

  • Saadé, R., & Bahli, B. (2005). The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: an extension of the technology acceptance model. Information & Management, 42(2), 317–327.

    Article  Google Scholar 

  • Saadé, R. G., & Kira, D. (2009). Computer anxiety in e-learning: The effect of computer self-efficacy. Journal of Information Technology Education: Research, 8, 177–191.

    Article  Google Scholar 

  • Šebjan, U., & Tominc, P. (2015). Impact of support of teacher and compatibility with needs of study on usefulness of SPSS by students. Computers in Human Behavior, 53, 354–365.

    Article  Google Scholar 

  • Tarhini, A., Arachchilage, N. A. G., & Abbasi, M. S. (2015). A critical review of theories and models of technology adoption and acceptance in information system research. International Journal of Technology Diffusion (IJTD), 6(4), 58–77.

    Article  Google Scholar 

  • Teo, T., & Zhou, M. (2014). Explaining the intention to use technology among university students: a structural equation modeling approach. Journal of Computing in Higher Education, 26(2), 124–142.

    Article  Google Scholar 

  • Thong, J. Y., Hong, S. J., & Tam, K. Y. (2006). The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. International Journal of Human-Computer Studies, 64(9), 799–810.

    Article  Google Scholar 

  • Tsai, C. H., & Yen, J. C. (2014). The augmented reality application of multimedia technology in aquatic organisms instruction. Journal of Software Engineering and Applications, 7(9), 745–755.

    Article  Google Scholar 

  • Valtonen, T., Kukkonen, J., Kontkanen, S., Sormunen, K., Dillon, P., & Sointu, E. (2015). The impact of authentic learning experiences with ICT on pre-service teachers’ intentions to use ICT for teaching and learning. Computers & Education, 81, 49–58.

    Article  Google Scholar 

  • Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342–365.

    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, 425–478.

    Article  Google Scholar 

  • Verma, P., & Sinha, N. (2018). Integrating perceived economic wellbeing to technology acceptance model: The case of mobile based agricultural extension service. Technological Forecasting and Social Change, 126, 207–216.

    Article  Google Scholar 

  • Wiest, L. R. (2001). The role of computers in mathematics teaching and learning. Computers in the Schools, 17(1–2), 41–55.

    Article  Google Scholar 

  • Wojciechowski, R., & Cellary, W. (2013). Evaluation of learners’ attitude toward learning in ARIES augmented reality environments. Computers & Education, 68, 570–585.

    Article  Google Scholar 

  • Xie, B. (2003). Older adults, computers, and the Internet: future directions. Gerontechnology, 2(4), 289–305. https://doi.org/10.4017/gt.2003.02.04.002.00.

    Article  Google Scholar 

  • Xu, F., & Du, J. T. (2018). Factors influencing users’ satisfaction and loyalty to digital libraries in Chinese universities. Computers in Human Behavior, 83, 64–72.

    Article  Google Scholar 

  • Yousef, D. A. (2017). Organizational commitment, job satisfaction and attitudes toward organizational change: a study in the local government. International Journal of Public Administration, 40(1), 77–88.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Health Sciences, Department of Healthcare Management, Afyonkarahisar Health Sciences University, Afyonkarahisar, Turkey

    Emin Ibili

  2. School of Information Technology & Mathematical Sciences, The University of South Australia, Adelaide, Australia

    Dmitry Resnyansky & Mark Billinghurst

Authors
  1. Emin Ibili
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dmitry Resnyansky
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mark Billinghurst
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Emin Ibili.

Ethics declarations

This research was supported by the postdoctoral research programme (BİDEB 2219) of The Scientific and Technological Research Council of Turkey (TUBITAK).

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

Table 8 Measurement of the variables
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ibili, E., Resnyansky, D. & Billinghurst, M. Applying the technology acceptance model to understand maths teachers’ perceptions towards an augmented reality tutoring system. Educ Inf Technol 24, 2653–2675 (2019). https://doi.org/10.1007/s10639-019-09925-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10639-019-09925-z

Keywords

Search

Navigation