Studying Factors Influencing Intention to Use Hybrid Education: A Case Study of Visual Communication Design Major Students at Chengdu Vocational University of Art
Abstract
Background and Aim: Hybrid teaching follows the reform trend of China's vigorous development of online education and hybrid education. The Chengdu Vocational University of Art is the earliest art vocational university in China, and the visual communication major is the advantageous major of the university. The program of this major adopts a hybrid education model combining MOOC online courses and offline education. This study uses TAM, ASCI, UTAUT, and ECM theories to construct a model to analyze the factors affecting the hybrid education learning satisfaction of visual communication students, whose variables include virtual classroom quality, instructor characteristics, student expectations, course design quality, and course content quality.
Materials and Methods: A purposive sampling method was used to distribute questionnaires to 571 students. Confirmatory factor analysis (CFA) and Structural Equation Modeling (SEM) were used to analyze the reliability, validity, goodness of fit, and hypothesis testing of the model.
Results: Student satisfaction was found to greatly influence intention to use through the study. In addition, instructor characteristics and classroom quality had a supportive relationship on perceived usefulness, student expectations, course content, and perceived usefulness had a supportive relationship on satisfaction, while course design quality had no significant effect on satisfaction.
Conclusion: School management and teaching teams should pay attention to the key influences on the intention of hybrid education and design programs based on research findings to help students understand the effectiveness of hybrid education and improve the quality of education.
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References
Ahmed, H.M. (2010). Hybrid E-learning acceptance model: Learner perceptions. Decision Sciences Journal of Innovative Education, 8 (2), 313-346. https://doi.org/10.1111/j.1540-4609.2010.00259.x
Al-Busaidi, K.A. (2013). An empirical investigation linking learners’ adoption of blended learning to their intention of full E-lEarning. Behavior & Information Technology, 32 (11), 1168-1176. https://doi.org/10.1080/0144929x.2013.774047
Alrousan, M.K., Al-Madadha, A., Al Khasawneh, M.H., & Adel Tweissi, A. (2021). Determinants of virtual classroom adoption in Jordan: The case of Princess Sumaya University for Technology. Interactive Technology and Smart Education, 19 (2), 121-144. https://doi.org/10.1108/itse-09-2020-0211
Arbaugh, J.B. (2000). Virtual classroom characteristics and student satisfaction with internet-based MBA courses. Journal of Management Education, 24 (1), 32–54. https://doi.org/10.1177/105256290002400104
Bentler, P.M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107 (2), 238–246. https://doi.org/10.1037/0033-2909.107.2.238
Bhuasiri, W., Xaymoungkhoun, O., Zo, H., Rho, J.J., & Ciganek, A. P. (2012). Critical success factors for e-learning in developing countries: A comparative analysis between ICT experts and faculty. Computers & Education, 58 (2), 843-855. https://doi.org/10.1016/j.compedu.2011.10.010
Brown, T.A. (2015). Confirmatory factor analysis for applied research. The Guilford Press.
Cheng, Y. (2019). How does task-technology fit influence cloud-based E-learning continuance and impact? Education + Training, 61 (4), 480-499. https://doi.org/10.1108/et-09-2018-0203
Cheng, Y. (2020). Students' satisfaction and continuance intention of the cloud-based E-learning system: Roles of interactivity and course quality factors. Education + Training, 62 (9), 1037–1059. https://doi.org/10.1108/et-10-2019-0245
Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13 (3), 319. https://doi.org/10.2307/249008
Davis, F.D., & 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
DeLone, W.H., & McLean, E.R. (1992). Information systems success: The quest for the dependent variable. Information Systems Research, 3 (1), 60–95. https://doi.org/10.1287/isre.3.1.60
Fang, X., & Yang, C.X. (2016). Research on the Influencing Factors of Teachers' Intention of Teaching Behavior in Catechism Classes in Colleges and Universities, Open Education Research, 22 (2), 67-76
Fornell, C., Johnson, M.D., Anderson, E.W., Cha, J., & Bryant, B.E. (1996). The American customer satisfaction index: Nature, purpose, and findings. Journal of Marketing, 60 (4), 7. https://doi.org/10.2307/1251898
Gao, F. (2018). Full model analysis of factors influencing student satisfaction under blended learning mode. Doctoral dissertation, Shaanxi Normal University.
Gu, J. (2013). Leveraging on "catechism" to push China's education forward faster. Liaoning Education, 11, 1-10.
Hair, F., Money, H., Page, M., & Samouel, P. (2007). Research Methods for Business, Education + Training, 49 (4), 336-337.
Hair, J.F., Anderson, R.E., Tatham, R.L., & Black, W.C. (2010). Multivariate Data Analysis, 6th ed. Upper Saddle River, NJ: Prentice Hall.
Hair, J.F., Black., W.C., Babin., B.J., Anderson., R.E., & L.Tatham, R. (2006). Multivariant Data Analysis. Pearson International Edition.
Hair, J.F., Hult, G.T.M., Ringle, C.M., & Sarstedt, M. (2013). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Sage, Thousand Oaks Press.
Ioannidou, O., & Erduran, S. (2022). Policymakers’ Views of Future-Oriented Skills in Science Education. Frontiers in Education. 7, 910128. Doi: 10.3389/feduc.2022.910128.
Israel, G.D. (1992). Determining Sample Size. University of Florida Cooperative Extension Service. Institute of Food and Agriculture Sciences, EDIS, Florida.
Kester, L., Sloep, P.B., Rosmalen, P.V., Brouns, F., Kone, M., & Koper, R. (2007). Facilitating community building in learning networks through peer tutoring in ad hoc transient communities. International Journal of Web-Based Communities, 3 (2), 198-204. https://doi.org/10.1504/ijwbc.2007.014080
Lee, B., Yoon, J., & Lee, I. (2009). Learners’ acceptance of E-learning in South Korea: Theories and results. Computers & Education, 53 (4), 1320-1329. https://doi.org/10.1016/j.compedu.2009.06.014
Lee, Y. (2006). An empirical investigation into factors influencing the adoption of an e-learning system. Online Information Review, 30 (5), 517–541. https://doi.org/10.1108/14684520610706406
Lin, W., & Wang, C. (2012). Antecedences to continued intentions of adopting E-learning system in blended learning instruction: A contingency framework based on models of information system success and task-technology fit. Computers & Education, 58 (1), 88-99. https://doi.org/10.1016/j.compedu.2011.07.008
Liu, T.J., (2020). Research on mixed teaching mode of visual communication design under the background of big data. Art Education Research, 233 (22), 154–155.
Liu, I., Chen, M.C., Sun, Y.S., Wible, D., & Kuo, C. (2010). Extending the TAM model to explore the factors that affect intention to use an online learning community. Computers & Education, 54 (2), 600-610. https://doi.org/10.1016/j.compedu.2009.09.009
Malhotra, N., Hall, J., Shaw, M., & Oppenheim, P. (2004). Essentials of Marketing Research, An Applied Orientation. Pearson Education Australia.
Patricia R. Robertson, P.R. (2014). Comparing Student Performance and Satisfaction in Face-to-Face and Hybrid Formats for a Finance Course. American Journal of Educational Research, 2 (2), 73-77
Pedroso, R., Zanetello, L., Guimaraes, L., Pettenon, M., Goncalves, V., Scherer, J., Kessler, F., & Pechansky, F. (2016). Confirmatory factor analysis (CFA) of the crack use relapse scale (CURS). Archives of Clinical Psychiatry, 43 (3), 37-40
Pham, L., Limbu, Y.B., Bui, T.K., Nguyen, H.T., & Pham, H.T. (2019). Does E-learning service quality influence E-learning student satisfaction and loyalty? Evidence from Vietnam. International Journal of Educational Technology in Higher Education, 16 (1). https://doi.org/10.1186/s41239-019-0136-3
Roca, J.C., Chiu, C., & Martínez, F.J. (2006). Understanding E-learning continuance intention: An extension of the technology acceptance model. International Journal of Human-Computer Studies, 64 (8), 683-696. https://doi.org/10.1016/j.ijhcs.2006.01.003
Salkind, J. (2017). Exploring Research. 9th edition, Pearson Press.
Sica, C. & Ghisi, M. (2007). The Italian versions of the Beck Anxiety Inventory and d the Beck Depression Inventory-II: Psychometric properties and discriminant power. In M.A. Lange (Ed.), Leading-edge psychological tests and testing research (pp. 27-50). Nova
Sun, F. (2014). A study of factors influencing the willingness to use Massive Open Online Course (MOOC) platform. Doctoral dissertation, Hebei Normal University.
Tan, X., & Kim, Y. (2015). User acceptance of SaaS-based collaboration tools: A case of Google Docs. Journal of Enterprise Information Management, 28 (3), 423-442. https://doi.org/10.1108/jeim-04-2014-0039
Traub, R.E., & Rowley, G.L. (1991). NCME Instructional Module: Understanding Reliability. Educational Measurement: Issues and Practice, 10 (1), 37-45.
Turner, R.C., & Carlson, L. (2003). Indexes of item-objective congruence for multidimensional items. International Journal of Testing, 3 (2), 163-171. https://doi.org/10.1207/s15327574ijt0302_5
Wang, J., & Feng, X. (2019). Blended teaching based on catechism: mode, effect, and trend-analysis based on sci and eric databases. China University Teaching, 10, 7-16.
Yuan, S.H., & Liu, X. (2014). Current status and shared problems of MOOC practice in Chinese universities - A report from MOOC practice in Chinese universities. Research on Modern Distance Education, 4, 11-20.