ISSN: 2222-6990
Open access
Augmented reality (AR) offers an innovative learning space through an active interaction of real world with computer-assisted contextual layer of information to enhance learning experiences. AR is exciting to explore in which it can enhance a user’s impression of and interaction with real and virtual world. There have been some attempts to use AR for training and educational purposes; however the potential of AR in education remains unexplored as it seen as a future of education. In addition to the lack of information about AR in education especially in Malaysia, there is also inadequate information about the influencing factors in determining students’ decision to use AR in their learning process. Thus, the present paper aims to evaluate factors that might influence pre-service teachers’ decision in using AR technology through Mobile Augmented Reality Learning Cardiovascular (MARLCardio). A self-designed MARLCardio was used as an experimental tool in data collection process. All respondents were given a chance to explore an AR features in MARLCardio before they responds to a web-based survey. The respondents of the study are 459 pre-service teachers at three public universities in Malaysia with different field of study. All data gathered from the questionnaire was analysed using path coefficients in SmartPLS. As a result of this investigation, results revealed that attitude (ATT) is the main factor in determining the actual use of MARLCardio. Surprisingly, field of study (FOS) was not found as a moderating effect of any relationship.
Anastasiou, ?., Valkanos, E., Simos, G., Montgomery, A., & Karamitropoulos, L. (2013). Measuring Elementary School Principals’ and Vice-Principals’ Competence: Adjustment and Evaluation of Kelly Glodt’s Instrument in Greece. Multilingual Academic Journal of Education and Social Sciences, 1(1), 50–69.
Issham, I., Norbaya, S. A., & Gunasegaran, T. (2016). Mobile learning in Malaysian universities : Are students ready ? International Journal of Interactive Mobile Technologies, 17-23.
Chung, N., Lee, H., Kim, J.-Y., & Koo, C. (2017). The Role of Augmented Reality for Experience-Influenced Environments: The Case of Cultural Heritage Tourism in Korea. Journal of Travel Research, 1-17.
Goutzioupas, G., & Iordanides, G. (2013). “Micropolitics” and Secondary Education Teachers’ Evaluation in Greece. Multilingual Academic Journal of Education and Social Sciences, 1(1), 70– 85.
Hanafi, H. F., Said, C. S., Ariffin, A. H., Zainuddin, N. A., & Samsuddin, K. (2016). Using a collaborative mobile augmented reality learning application (coMARLA) to improve student learning. International Engineering Research and Innovation Symposium (pp. 1-9). Bristol: IOI Publishing.
Lazim, N. A., & Abd Rahman, K. A. (2015). State-of-the-art responses on augmented reality application in Malaysia. Alam Cipta, 29-34.
Azuma, R. (1997). A survey of augmented reality. Teleoperators and Virtual Environments, 355-385.
Lee, K.-F., Wu, M.-S., Hsieh, H.-C., & Chin, K.-Y. (2018). Augmented reality supported mobile self-guided system for enhancing authentic learning activities. Int. J. Cognitive Performance Support, 117-131.
Wojciechowski, R., & Cellary, W. (2013). Evaluation of learners' attitude towards learning in ARIES augmented reality environments. Computers & Education, 570-585.
Serio, D. A., Ibanez, M. B., & Kloos, C. D. (2013). Impact of an augmented reality system on students' motivation for visual art course. Computer & Education, 586-596.
Mat-jizat, J., Osman, J., Yahaya, R., & Samsudin, N. (2016). The use of augmented reality (ar) among tertiary level students : Perception and experience. Asian Journal of Sustainable Business and Society, 42-49.
Abd Majid, N. A., & Kamarudin Husain, N. (2014). Mobile learning application based on augmented reality for science subject : Isains. Journal of Engineering and Applied Sciences, 1455-1460.
Allen, K. R. (2016). Building bridges between the virtual and real: A study of augmented and virtual Realities in the museum space and the collaborations that produce them. United States: ProQuest LLC .
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. Management Information Systems Research Center,, 319-340.
Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology : Extending the unified theory of acceptance and use of technology. Mis Quarterly, 157-178.
Albashrawi, M., Kartal, H., Oztekin, A., & Motiwalla, L. (2017). The impact of subjective and objective experience on mobile banking usage : An analytical approach. Proceedings of the 50th Hawaii International Conference on System Sciences , 1161-1171.
Maruping, L. M., Bala, H., Venkatesh, V., & Brown, S. A. (2016). Going beyond intention : integrating behavioral expectation into the unified theory of acceptance and use of technology. Journal of the association for information science and technology, 1-15.12
Baydas, O., & Yilmaz, R. M. (2016). Pre-service teachers' intention to adopt mobile learning : A motivational model. British Journal of Educational Technology, 1-15.
Nysveen, H., & Pedersen, P. E. (2014). Consumer adoption of RFID-enabled services. Applying an extended UTAUT model. Information systems frontiers, 1-22.
Chang, C.-C., Yan, C.-F., & Tseng, J.-S. (2012). Perceived convenience in an extended technology acceptance model : Mobile technology and English learning for college students. Australasian Journal of Educational Technology, 809-826.
Isaias, P., Reis, F., Coutinho, C., & Lencastre, J. A. (2017). Empathic technologies for distance/mobile learning : An emperical research based on UTAUT. Interactive technology and Smart Education, 1-20.
Thomas, T. D., Singh, L., & Gaffar, K. (2013). The utility of the UTAUT model in explaining mobile learning adoption in higher education in Guyana. International Journal of Education and Development using Information and Communication Technology (IJEDICT), 71-85.
Teo, T. (2014). Modelling facebook usage among universities students in Thailand: The role of emotional attachment in an extended technology acceptance model. Interactive Learning Environments, 1-15.
Mou, J., Shin, D.-H., & Cohen, J. (2016). Understanding trust and perceived usefulness in the consumer acceptance of an e-service : a longitudinal investigation. Behaviour & Information Technology, 1-16.
Taylor, S., & Todd, P. A. (2001). Understanding information technology usage : A test of competing models. Information System Research, 144-175.
Li, R., Chung, T.-L., & Fiore, A. M. (2017). Factors affecting current users' attitude towards e-auctions in China: An extended TAM study. Journal of Retailing and Consumer Services, 19-29.
Bharati, V. J., & Srikanth, R. (2018). Modified UTAUT2 model for m-learning among. Int. J. Learning and Change, 5-19.
Guest , W., Wild, F., Vovk, A., Lefrere, P., Klemke, R., Fominykh, M., & Kuula, T. (2018). A Technology Acceptance Model for Augmented Reality and Wearable Technologies. Journal of Universal Technology Science, 192-219.
Shang, D., & Wu, W. (2017). Understanding mobile shopping consumers' continuance intention. Industrial Management & Data Systems, 213-227.
Paulo, M. M., Rita, P., Oliveira, T., & Moro, S. (2017). Understanding mobile augmented reality adoption in a consumer context. Journal of Hospitality and Tourism Technology, 1-17.
Madigan, R., Louw, T., Wilbrink, M., Schieben, A., & Merat, N. (2017). What influences the decision to use automated public transport? Using UTAUT to understand public acceptance of automated road transport systems. Transportation Research Part F, 55-64.
Maillet, E., Mathieu, L., & Sicotte, C. (2015). Modeling factors explaining the acceptance, actual use and satisfaction of nurses using an Electronic Patient Record in acute care settings: An extension of the UTAUT. International Journal of Medical Informatics, 36-47.
Bandura, A. (2001). Organisational application of social cognitive theory. Australian Journal of Management, 275-302.
Ha, Y. W., Kim, J., Libaque-Saenz, C. F., Chang, Y., & Park, M.-C. (2015). Use and gratifications of mobile SNSs : Facebook and KakaoTalk in Korea. Telematics and Informatics, 425-438.
Gao, L., Waechter, K. A., & Bai, X. (2015). Understanding consumers' continuance intention towards mobile purchase : A theoretical framework and empirical study - A case of China. Computer in Human Behaviour, 249-262.
Huang, Y., Li, H., & Fong, R. (2015). Using augmented reality in early art education : a case study in Hong Kong kindergarten. Early child development and care, 1-16.
Salmi, H., Thuneberg, H., & Vainikainen, M.-P. (2016). Making the invisible observable by Augmented Reality in informal science education context. International Journal of Science Education, 1-17.
Tussyadiah, I., Jung, T. H., & Dieck, M. T. (2017). Embodient of wearable augmeted reality technology in tourism experiences. Journal of Travel Research, 1-15.
Solano, A., Ugalde, F., Gomez, J., & Sanchez, L. (2017). An augmented reality application to enhance the children's engagement in an early development method for mathematics literacy. Advances in Intelligent Systems and Computing , 405-414.
Giglioli, I. C., Pallavicini, F., Pedroli, E., Serino, S., & Riva, G. (2015). Augmented reality : A brand new challenge for the assessment and treatment of psychological disorders. Computational and Mathematical Methods in Medicine, 1-12.
White, G., Cordato, D., O'Rourke, F., Mendis, R., Ghia, D., & Chan, D. (2012). Validation of the stroke rehabilitation motivation scale: A pilot study. Asian Journal of Gerontology & Geriatrics , 80-87.
Malaysian Communications and Multimedia Commision. (2015). Komunikasi & Multimedia: Buku Maklumat Statistik. Selangor: Malaysian Communications and Multimedia Commision (MCMC).
Bhattacherjee, A. (2001). Understanding information systems continuance : An expectation-comfirmation model. MIS Quarterly, 351-370.
Pappas, I. O., Pateli, A. G., Giannakos, M. N., & Chrissikopoulos, V. (2014). Moderating effects of online shopping experience on customer satisfation and repurchase intentions. International Journal of Retail & Distribution Management, 187-204.
Tarhini, A., Hone, K., & Liu, X. (2014). The effects of individual differences on e-learning users' behaviour in developing countries : A structural equation model. Computer in Human Behavior, 153-16.
In-Text Citation: (Nizar, N. N. M., Rahmat, & Damio, 2020)
To Cite this Article: Nizar, N. N. M., Rahmat, M. K., & Damio, S. M. (2020). Evaluation of Pre-service Teachers’ Actual use toward Augmented Reality Technology through MARLCardio. International Journal of Academic Research in Business and Social Sciences. 10(11), 1091-1101.
Copyright: © 2020 The Author(s)
Published by HRMARS (www.hrmars.com)
This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at: http://creativecommons.org/licences/by/4.0/legalcode