Abstract
Recently, due to the coronavirus pandemic, we are experiencing a revolution that is transforming the way, the education has now shifted to an “physical plus digital” or “phygital” multimodal.
This paper analyses the students’ behavioral intention to the phygital learning, meaning how students use online learning platform (e.g. Moodle), collaboration application (e.g. Microsoft teams), chat application (e.g. Wechat) and device (e.g. smartphone, laptop) of a course.
For the evaluation purpose is followed by using the Semantic Differential Technique to distinguish the usage attitude of computer and smartphone. The Usage Questionnaire is followed by the System Usability Scale (SUS), which is a Human Computer Interaction (HCI) based approach, and the Technology Acceptance Model (TAM), which is an Information Systems (IS) based approach. The sample size consisted of 68 participants completed the survey questionnaire measuring their responses to perceived usefulness (PU), perceived ease of use (PEOU) and attitudes towards usage (ATU).
Through simultaneously both these instruments in one work for the purpose of usability evaluation. By doing so, this work attempts to streamline and unify the process of usability evaluation. Results that are obtained from a large-scale survey of university students show the attitudes towards usage on phygital learning. Moreover, this work also considers the digital-divide aspect (mobile v.s. web environment) whether it has any effect on the perceived usability. Results show that the multiple education modal could reduce the stress on the learning.
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References
Ajzen, I., Fishbein, M.: Attitudes and the attitude-behavior relation: reasoned and automatic processes. In: Stroebe, W., Hewstone, M. (eds.) European Review of Social Psychology, pp. 1–331 (2000)
Baylor, A.L., Ritchie, D.: What factors facilitate teacher skill, teacher morale, and perceived student learning in technology- USING classrooms? Comput. Educ. 39(4), 395–414 (2002). https://doi.org/10.1016/S0360-1315(02)00075-1
Davis, D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13(3), 319–340 (1989)
Gong, M., Xu, Y., Yu, Y.: An enhanced technology acceptance model for web-based learning. J. Inf. Syst. Educ. 15(4), 365–373 (2004). http://www.highbeam.com/doc/1P3-793505851.html
Hair, J.F., Anderson, R.E., Tatham, R.L., Black, W.C.: Multivariate Data Analysis, 5th edn. Prentice Hall International Inc., New Jersey (1998)
Hu, P.J., Chau, P.Y.K., Sheng, O.R.L., Tam, K.Y.: Examining the technology acceptance model using physician acceptance of telemedicine technology. J. Manag. Inf. Syst. 16(2), 91–112 (1999)
Junus, I.S., Santoso, H.B., Isal, R.Y.K., Utomo, A.Y.: Usability evaluation of the student centered e- learning environment. Int. Rev. Student-Centered E-Learn. Environ. 16(4), 62–82 (2015)
Kramer, A.: Retail Business Models for the Future: The Convergence of Physical and Digital Channels Blog post (2014). https://www.capgemini.com/blog/capping-it-off/2014/01/retail-business-models-for-the-future-the-convergence-of-physical-and. Accessed 20 June 2021
Lewis, J.R.: Usability: Lessons learned and yet to be learned. Int. J. Hum.-Comput. Interact. 30(9), 663–684 (2014)
Lewis, J.R.: Measuring perceived usability: the CSUQ, SUS, and UMUX. Int. J. Hum.-Comput. Interact. 34(12), 1148–1156 (2018)
Lewis, J.R.: The system usability scale: Past, present, and future. Int. J. Hum.-Comput. Interact. 34(7), 577–590 (2018)
Peres, S.C., Pham, T., Phillips, R.: Validation of the system usability scale (SUS): SUS in the wild. Proc. Hum. Factors Ergon. Soc. 57(1), 192–196 (2013)
Rizki, J.: What is Phygital Marketing? (2014). https://www.quora.com/What-is-Phygital-Marketing. Accessed 20 June 2021
Saunders, G., Klemming, F.: Integrating technology into a traditional learning environment. Act. Learn. High. Educ. 4(1), 74–86 (2003). https://doi.org/10.1177/1469787403004001006
Vate-U-Lan, P., Quigley, D., Masouras, P.: Phygital learning concept: from big to smart data. In: The Thirteenth International Conference on eLearning for Knowledge-Based Society, 15 December 2016
Venkatesh, V., Morris, M.G., Davis, F.D., Davis, G.B.: User acceptance of information technology: toward a unified view. MIS Q. 27(3), 425–478 (2003). http://www.jstor.org/pss/30036540
Wang, L.Y.K., Lew, S.L., Lau, S.H., Leow, M.C.: Usability factors predicting continuance of intention to use cloud E- learning application. Heliyon 5(6), e01788 (2019). https://doi.org/10.1016/j.heliyon.2019.e01788
Yildirim, S.: Effects of an educational computing course on preservice and Inservice teachers: a discussion and analysis of attitudes and use. J. Res. Comput. Educ. 32(4), 479–495 (2000)
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Huang, SY., Chu, SL. (2022). The Influence of Phygital Learning: The User Expectations of Perceived Usability of Practical and Theoretical Courses. In: Stephanidis, C., Antona, M., Ntoa, S., Salvendy, G. (eds) HCI International 2022 – Late Breaking Posters. HCII 2022. Communications in Computer and Information Science, vol 1655. Springer, Cham. https://doi.org/10.1007/978-3-031-19682-9_19
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