Abstract
This action research study has two aims: (i) to develop a wearable virtual reality language-learning platform for English for specific purposes and (ii) to examine student learning effectiveness and the relationship between self-efficacy and behavioral intention. The participants are 131 university students in Taiwan. A model of wearable virtual reality language-learning based on structural equation modeling is built. A path analysis indicates that self-efficacy directly influences students’ perceptions of ease of use of the wearable virtual reality technology and indirectly influences perceived usefulness, attitude, and behavioral intention. Student self-efficacy is slightly above the moderate level. Gender and English proficiency have an effect on student self-efficacy and behavioral intention, whereas students’ online and virtual reality learning experiences have no significant effect on these variables. This new environment is shown to facilitate student learning: the pretest and posttest results indicate improvements in lexical and semantic knowledge, reading comprehension, and syntax. The study implies that since self-efficacy is a crucial determinant in student technology use, educators should enhance student self-efficacy in order to encourage them to try innovative technology.
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Data Availability
The datasets generated and analyzed during the current study available at the following link. https://drive.google.com/drive/folders/14bE5127rnwTfr1JpuA-p7vfTJBCbN7PJ?usp=sharing
References
Adesida, Y., Papi, E., & McGregor, A. H. (2019). Exploring the role of wearable technology in sport kinematics and kinetics: A systematic review. Sensors, 19(7), 1597.
Al-Emran, M., Al-Maroof, R., Al-Sharafi, M. A., & Arpaci, I. (2020). What impacts learning with wearables? An integrated theoretical model. Interactive learning environments, 1-21.
Ali, R. A., & Arshad, M. R. M. (2016). Perspectives of students’ behavior towards mobile learning (M-learning) in Egypt: An extension of the UTAUT model. Engineering, Technology & Applied Science Research, 6(4), 1109–1114.
Alkhuwaylidee, A. R. (2019). Extended unified theory of acceptance and use of technology (UTAUT) for e-learning. Journal of Computational and Theoretical Nanoscience, 16(3), 845–852.
Allcoat, D., & von Mühlenen, A. (2018). Learning in virtual reality: Effects on performance, emotion and engagement. Research in Learning Technology, 26, 2140. https://doi.org/10.25304/rlt.v26.2140
Almaiah, M. A., Alamri, M. M., & Al-Rahmi, W. (2019). Applying the UTAUT model to explain the students’ acceptance of mobile learning system in higher education. IEEE Access, 7, 174673–174686.
Ayub, A. F. M., Zaini, S. H., Luan, W. S., & Jaafar, W. M. W. (2017). The influence of mobile self-efficacy, personal innovativeness and readiness towards students’ attitudes towards the use of mobile apps in learning and teaching. International Journal of Academic Research in Business and Social Sciences, 7(14), 364–374.
Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37(2), 122–147.
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall.
Barriga, N. A. (2019). A short introduction to procedural content generation algorithms for videogames. International Journal on Artificial Intelligence Tools, 28(02), 1930001.
Bower, M., & Sturman, D. (2015). What are the educational affordances of wearable technologies? Computers & Education, 88, 343–353.
Bozbayındır, F. (2016). Developing of a school transparency scale: A study on validity and reliability. International Online Journal of Educational Sciences, 8(4), 46–58.
Campbell, D. T., & Stanley, J. (1963). Experimental and quasi-experimental designs for research on teaching. Houghton Mifflin.
Chang, H. S., Lee, S. C., & Ji, Y. G. (2016). Wearable device adoption model with TAM and TTF. International Journal of Mobile Communications, 14(5), 518–537.
Chau, K. Y., Lam, M. H. S., Cheung, M. L., Tso, E. K. H., Flint, S. W., Broom, D. R., Tse, G., & Lee, K. Y. (2019). Smart technology for healthcare: Exploring the antecedents of adoption intention of healthcare wearable technology. Health Psychology Research, 7(1:8099), 33–39. https://doi.org/10.4081/hpr.2019.8099
Chavez, B., & Bayona, S. (2018, March). Virtual reality in the learning process. In world conference on information systems and technologies (pp. 1345-1356). Springer, Cham.
Chen, Y. L. (2016). The effects of virtual reality learning environment on student cognitive and linguistic development. The Asia-Pacific Education Researcher, 25(4), 637–646.
Chen, C. M., & Tsai, Y. N. (2009, July). Interactive location-based game for supporting effective English learning. In proceedings of 2009 ESIAT international conference on environmental science and information application technology (Vol. 3, pp. 523-526). IEEE.
Churchill, D. (2017). Emerging possibilities for design of digital resources for learning. In digital resources for learning (pp. 227–246). Springer, .
Chutipascharoen, A. C. A., & Chaichomchuen, S. (2019). The development of constructivism learning model by using virtual reality simulation game based on English to improve English listening skills and speaking skills for junior high schools students. KKU Research Journal (Graduate Studies) Humanities and Social Sciences, 7(3), 79–90.
Cohen, L., Manion, L., & Morrison, K. (2007). Research methods in education. Routledge.
Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19, 189–211.
Cunningham, E. G. (2008). A practical guide to structural equation modeling using AMOS. Statsline.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
De la Guía, E., Camacho, V. L., Orozco-Barbosa, L., Luján, V. M. B., Penichet, V. M., & Pérez, M. L. (2016). Introducing IoT and wearable technologies into task-based language learning for young children. IEEE Transactions on Learning Technologies, 9(4), 366–378.
Doll, W. J., Xia, W., & Torkzadeh, G. (1994). A confirmatory factor analysis of the end-user computing satisfaction instrument. MIS Quarterly, 18(4), 453–461.
Dreher, C., Reiners, T., Dreher, N., & Dreher, H. (2009). Virtual worlds as a context suited for information systems education: Discussion of pedagogical experience and curriculum design with reference to second life. Journal of Information Systems Education, 20(2), 211–224.
Duffy, T. M., & Jonassen, D. H. (Eds.). (2013). Constructivism and the Technology of Instruction: A conversation. Routledge.
Dugard, P., & Todman, J. (1995). Analysis of pre-test-post-test control group designs in educational research. Educational Psychology, 15(2), 181–198.
Edwards, B. I., Bielawski, K. S., Prada, R., & Cheok, A. D. (2019). Haptic virtual reality and immersive learning for enhanced organic chemistry instruction. Virtual Reality, 23(4), 363–373.
Errichiello, L., Micera, R., Atzeni, M., & Del Chiappa, G. (2019). Exploring the implications of wearable virtual reality technology for museum visitors' experience: A cluster analysis. International Journal of Tourism Research, 21(5), 590–605.
Ezenwoke, A., Ezenwoke, O., Adewumi, A., & Omoregbe, N. (2016). Wearable technology: Opportunities and challenges for teaching and learning in higher education in developing countries. In Proceedings of INTED2016 Conference (pp.1872-1879), March 7–9, 2016, Valencia, .
Ferrell, J. B., Campbell, J. P., McCarthy, D. R., McKay, K. T., Hensinger, M., Srinivasan, R., Zhao, X., Wurthmann, A., Li, J., & Schneebeli, S. T. (2019). Chemical exploration with virtual reality in organic teaching laboratories. Journal of Chemical Education, 96(9), 1961–1966.
Garnier-Villarreal, M., & Jorgensen, T. D. (2020). Adapting fit indices for Bayesian structural equation modeling: Comparison to maximum likelihood. Psychological Methods, 25(1), 46–70.
Global Augmented Reality I.G. (2019). https://www.prnewswire.com/news-releases/global-augmented-reality-ar-and-virtual-reality-vr-market-in-healthcare-market-to-reach-11-14-billion-by-2025%2D%2D300831306.html
Goodhue, D. L., Lewis, W., & Thompson, R. (2012). Does PLS have advantages for small sample size or non-normal data? MIS Quarterly, 36(3), 981–1001.
GovindAarajan, P. B., & Krishnan, A. R. (2019). A study on influence of web quality and self efficacy on massive open online courses (MOOCs) technology adoption by extending the UTAUT model with reference to student MOOC users. Shanlax International Journal of Management, 7(2), 47–53.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis: A global perspective. Upper Saddle, NJ : Pearson.
Hashim, R. A., & Alias, N. (2016). Medicating effect of self-efficacy in the relationship between technology competency and learners’ acceptance and usage of e-learning portal. In Proceedings of the Annual Conference of the Asian Association of Open Universities, Kuala Lumpur, Malaysia (pp. 1–13).
He, J., & Freeman, L. A. (2019). Are men more technology-oriented than women? The role of gender on the development of general computer self-efficacy of college students. Journal of Information Systems Education, 21(2), 203–212.
Herz, M., & Rauschnabel, P. A. (2019). Understanding the diffusion of virtual reality glasses: The role of media, fashion and technology. Technological Forecasting and Social Change, 138, 228–242.
Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.
Huang, Y. C., Backman, S. J., Backman, K. F., McGuire, F. A., & Moore, D. (2019). An investigation of motivation and experience in virtual learning environments: A self-determination theory. Education and Information Technologies, 24(1), 591–611.
Inoue, Y. (2007). Concepts, applications, and research of virtual reality learning environments. International Journal of Social Sciences, 2(1), 1–7.
Jacobs, J. V., Hettinger, L. J., Huang, Y. H., Jeffries, S., Lesch, M. F., Simmons, L. A., Verma, S. K., & Willetts, J. L. (2019). Employee acceptance of wearable technology in the workplace. Applied Ergonomics, 78, 148–156.
Johnson, L., Adams Becker, S., Cummins, M., Estrada, V., & Freeman, A. (2015). NMC horizon report: 2015 higher (Education ed.). The New Media Consortium.
Kalyuga, S. (2007). Enhancing instructional efficiency of interactive e-learning environments: A cognitive load perspective. Educational Psychology Review, 19(3), 387–399.
Keengwe, J. (Ed.). (2017). Handbook of research on Mobile technology, constructivism, and meaningful learning. IGI Global.
Kenny, D. A., Kaniskan, B., & McCoach, D. B. (2015). The performance of RMSEA in models with small degrees of freedom. Sociological Methods & Research, 44(3), 486–507.
Kerlinger, F. N., & Lee, H. B. (1999). Foundations of behavioral research (4th ed.). Hancourt College Publishers.
Kim, S. J., & Cho, J. (2019). Technological and personal factors of determining the acceptance of wrist-worn smart devices. Asian Journal for Public Opinion Research, 7(3), 143–168.
Kim, K. J., & Shin, D. H. (2015). An acceptance model for smart watches: Implications for the adoption of future wearable technology. Internet Research, 25(4), 527–541.
Kline, R. B. (1998). Principles and practice of structural equation modeling. New York, NY : The Guilford Press.
Lee, E. A. L., & Wong, K. W. (2014). Learning with desktop virtual reality: Low spatial ability learners are more positively affected. Computers & Education, 79, 49–58.
Li, J., Ma, Q., Chan, A. H., & Man, S. S. (2019). Health monitoring through wearable technologies for older adults: Smart wearables acceptance model. Applied Ergonomics, 75, 162–169.
Liu, H., Ma, H., & Chen, D. (2020, June). Design of Limb Rehabilitation Training System Based on virtual reality technology. In proceedings of 2020 IEEE 4th information technology, networking, electronic and automation control conference (ITNEC) (Vol. 1, pp. 1676-1679).
Lounaskorpi, E. (2019). Developing new VR possibilities for the sports and fitness sector in Finland (pp.1-60). MUBBAThesis: Multilingual management assistants.
Macdonald, E. M., Perrin, B. M., Hyett, N., & Kingsley, M. I. (2019). Factors influencing behavioural intention to use a smart shoe insole in regionally based adults with diabetes: A mixed methods study. Journal of Foot and Ankle Research, 12(1), 29.
Makransky, G., & Petersen, G. B. (2019). Investigating the process of learning with desktop virtual reality: A structural equation modeling approach. Computers & Education, 134, 15–30.
Martín-Gutiérrez, J., Mora, C. E., Añorbe-Díaz, B., & González-Marrero, A. (2017). Virtual technologies trends in education. EURASIA Journal of Mathematics Science and Technology Education, 13(2), 469–486.
McFaul, H., & FitzGerald, E. (2020). A realist evaluation of student use of a virtual reality smartphone application in undergraduate legal education. British Journal of Educational Technology, 51(2), 572–589.
Najafi, B., Armstrong, D. G., & Mohler, J. (2013). Novel wearable technology for assessing spontaneous daily physical activity and risk of falling in older adults with diabetes. Journal of diabetes science and Technology, 7(5), 1147–1160.
Nugent, G., Barker, B., Lester, H., Grandgenett, N., & Valentine, D. (2019). Wearable textiles to support student STEM learning and attitudes. Journal of Science Education and Technology, 28(5), 470–479.
Page, T. (2015). A forecast of the adoption of wearable technology. International, a forecast of the adoption of wearable technology. Journal of Technology Diffusion, 6(2), 12–29.
Parong, J., & Mayer, R. E. (2018). Learning science in immersive virtual reality. Journal of Educational Psychology, 110(6), 785–797.
Radianti, J., Majchrzak, T. A., Fromm, J., & Wohlgenannt, I. (2020). A systematic review of immersive virtual reality applications for higher education: Design elements, lessons learned, and research agenda. Computers & Education, 147, 103778.
Rehbein, F., Staudt, A., Hanslmaier, M., & Kleim, S. (2016). Video game playing in the general adult population of Germany: Can higher gaming time of males be explained by gender specific genre preferences? Computers in Human Behavior, 55, 729–735.
Santos, J. R. A. (1999). Cronbach’s alpha: A tool for assessing the reliability of scales. Journal of Extension, 37(2), 1–5.
Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research Online, 8(2), 23–74.
Shadiev, R., & Yang, M. (2020). Review of studies on technology-enhanced language learning and teaching. Sustainability, 12(2), 524.
Shadiev, R., Hwang, W. Y., & Liu, T. Y. (2018). A study of the use of wearable devices for healthy and enjoyable English as a foreign language learning in authentic contexts. Journal of Educational Technology & Society, 21(4), 217–231.
Shen, C. W., Ho, J. T., Ly, P. T. M., & Kuo, T. C. (2019). Behavioral intentions of using virtual reality in learning: Perspectives of acceptance of information technology and learning style. Virtual Reality, 23(3), 313–324.
Smedley, T. M., & Higgins, K. (2005). Virtual technology: Bringing the world into the special education classroom. Intervention in School and Clinic, 41(2), 114–119.
Tully, J., Dameff, C., & Longhurst, C. A. (2020). Wave of wearables: Clinical management of patients and the future of connected medicine. Clinics in Laboratory Medicine, 40(1), 69–82.
Vandenberg, R. J. (2006). Statistical and methodological myths and urban legend. Organzizational Research Methods, 9(2), 194–201.
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.
Vesisenaho, M., Juntunen, M., Häkkinen, P., Pöysä-Tarhonen, J., Miakush, I., Fagerlund, J., & Parviainen, T. (2019). Virtual reality in education: Focus on the role of emotions and physiological reactivity. Journal of Virtual Worlds Research, 12(1), 1–15.
Wang, Y., & Braman, J. (2009). Extending the classroom through second life. Journal of Information Systems Education, 20(2), 235–247.
Warschauer, M., & Healey, D. (1998). Computers and language learning: An overview. Language Teaching, 31(2), 57–71.
Yang, J. C., Chen, C. H., & Jeng, M. C. (2010). Integrating video-capture virtual reality technology into a physically interactive learning environment for English learning. Computers & Education, 55(3), 1346–1356.
Yau, H. K., & Leung, Y. F. (2016, March). Gender difference of self-efficacy and attitudes towards the use of technology in learning in Hong Kong higher education. In proceedings of the IMECS 2016 international MultiConference of engineers and computer scientists (Vol. 2, pp. 819-821). Newswood limited.
Yildirim, G., Elban, M., & Yildirim, S. (2018). Analysis of use of virtual reality technologies in history education: A case study. Asian Journal of Education and Training, 4(2), 62–69.
Zhao, J., LaFemina, P., Carr, J., Sajjadi, P., Wallgrün, J. O., & Klippel, A. (2020, March). Learning in the field: Comparison of desktop, immersive virtual reality, and actual field trips for place-based STEM education. In 2020 IEEE conference on virtual reality and 3D user interfaces (VR) (pp. 893-902). IEEE.
Zlatkin-Troitschanskaia, O., Shavelson, R. J., & Pant, H. A. (2018). Assessment of learning outcomes in higher education. Handbook on measurement, assessment, and evaluation higher education, 686-698.
Acknowledgements
This work was entirely supported by grants from the Ministry of Science and Technology, Taiwan (Projects Ref. No. MOST 107-2410-H-262-003; MOST 107-2622-H-262 -002 -CC3; and MOST107-2622-H-262-001-CC3).
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Hsu, CC., Chen, YL., Lin, CY. et al. Cognitive development, self-efficacy, and wearable technology use in a virtual reality language learning environment: A structural equation modeling analysis. Curr Psychol 41, 1618–1632 (2022). https://doi.org/10.1007/s12144-021-02252-y
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DOI: https://doi.org/10.1007/s12144-021-02252-y