Vocational College Pre-School Education Students’ Satisfaction Towards the Use of the Superstar Application to Teach Hands-On Art Class
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Abstract
Mobile learning was popular during the epidemic, and a number of schools in China used the Superstar Application to assist distance learning. Therefore, it is relevant to investigate students' satisfaction with the use of the Superstar application in the classroom to enhance student learning.
The purpose of the study was 1) To identify the factors that affect vocational colleges students' satisfaction with the Superstar application, and 2) To determine the level of vocational college students’ satisfaction towards the Superstar Application.
This research utilized a quantitative approach, using a questionnaire as a survey tool to collect sample data from the target population. The purposive sampling strategy, inviting 415 students from Deyang Vocational College of Technical and Trade to participate in the research. Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) were employed to assess the model's fit and establish the causal relationships between the variables for hypothesis testing.
The research's conceptual framework was constructed upon the foundation of the Web-based Learning Environment Instrument (WEBLEI) and the Expectation-Confirmation Model (ECM), with seven variables being identified as access, interaction, response, result, perceived usefulness, confirmation, and satisfaction. It was found that six of the seven hypotheses proposed were proven to achieve the research objectives, while the hypothesis between confirmation and satisfaction was not proven. The results of the study showed that the factors affecting students' satisfaction with the Superstar Application in vocational and technical colleges were access, interaction, response, result, and perceived usefulness; with perceived usefulness being the strongest predictor directly affecting the Superstar Application; and the result variable had a significant effect on students' satisfaction with Superstar Application use. In addition, the study also found that although confirmation did not directly impact satisfaction, confirmation played a significant role in perceived usefulness.
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References
Al-Emran, M., Arpaci, I., & Salloum, S. A. (2020). An empirical examination of continuous intention to use m-learning: An integrated model. Education and Information Technologies, 25 (4), 2899-2918. https://doi.org/10.1007/s10639-019-10094-2
Allen, I. E., & Seaman, J. (2008). Staying the course: Online education in the United States, 2008. PO Box1238, Newburyport, MA 01950.
Becker, H. J., & Ravitz, J. (1999). The influence of computer and internet use on teachers’ pedagogical practices and perceptions. Journal of research on computing in education, 31 (4), 356-384.
Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological bulletin, 88 (3), 588.
Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 351-370.
Biswas, B., Roy, S. K., & Roy, F. (2020). Students perception of mobile learning during COVID-19 in Bangladesh: . University student perspective.
Bordoloi, R., Das, P., & Das, K. (2021). Perception towards online/blended learning at the time of Covid-19 pandemic: an academic analytics in the Indian context. Asian Association of Open Universities Journal, 16 (1), 41-60. https://doi.org/10.1108/AAOUJ-09-2020-0079
Chandra, V., & Fisher, D. L. (2009). Students’ perceptions of a blended web-based learning environment. Learning Environments Research, 12, 31-44.
Chang, V., & Fisher, D. (2001). The validation and application of a new learning environment instrument to evaluate online learning in higher education. Proceedings of the Australian Association for Research in Education conference 2001,
Chang, V., & Fisher, D. (2003). The validation and application of a new learning environment instrument for online learning in higher education. In Technology-rich learning environments: A future perspective (pp. 1-20).
Cho, G., Hwang, H., Sarstedt, M., & Ringle, C. M. (2020). Cutoff criteria for overall model fit indexes in generalized structured component analysis. Journal of marketing analytics, 8 (4), 189-202. https://doi.org/10.1057/s41270-020-00089-1
Dabholkar, P. A., Shepherd, C. D., & Thorpe, D. I. (2000). A comprehensive framework for service quality: an investigation of critical conceptual and measurement issues through a longitudinal study. Journal of Retailing, 76 (2), 139-173. https://doi.org/https:// doi.org/10.1016/S0022-4359(00)00029-4
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13 (3), 319-340. https://doi.org/10.2307/ 249008
Demirtaş, İ., Ayyıldız, S., Ayyıldız, B., & Kuş, K. Ç. (2021). Distance education during social isolation: an evaluation of student attitudes and perceptions using the web-based learning environment instrument (WEBLEI). Anatomy, 15 (2), 163-170. https://doi.org/ 10.2399/ana.21.928791
Dubey, P., & Sahu, K. K. (2021). Students' perceived benefits, adoption intention and satisfaction to technology-enhanced learning: examining the relationships. Journal of Research in Innovative Teaching & Learning, 14 (3), 310-328. https://doi.org/10.1108/ JRIT-01-2021-0008
Goodman, S. N. (1999). Toward Evidence-Based Medical Statistics. 1: The P Value Fallacy. Annals of internal medicine, 130 (12), 995-1004. https://doi.org/10.7326/0003-4819-130-12-199906150-00008
Gupta, A., & Pathania, P. (2021). To study the impact of Google Classroom as a platform of learning and collaboration at the teacher education level. Education and Information Technologies, 26 (1), 843-857.
Hair Jr, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis. In Multivariate data analysis (pp. 785-785).
Kirkwood, A., & Price, L. (2014). Technology-enhanced learning and teaching in higher education: what is ‘enhanced’and how do we know? A critical literature review. Learning, media technology, 39 (1), 6-36.
Kumar Basak, S., Wotto, M., & Bélanger, P. (2018). E-learning, M-learning and D-learning: Conceptual definition and comparative analysis. E-Learning and Digital Media, 15 (4), 191-216. https://doi.org/10.1177/2042753018785180
Larsen, T. J., Sørebø, A. M., & Sørebø, Ø. (2009). The role of task-technology fit as users’ motivation to continue information system use. Computers in Human Behavior, 25 (3), 778-784.
Lee, Y., & Kwon, O. (2011). Intimacy, familiarity and continuance intention: An extended expectation–confirmation model in web-based services. Electronic Commerce Research and Applications, 10 (3), 342-357. https://doi.org/https://doi.org/10.1016/ j.elerap.2010.11.005
Lin, W. S., & Wang, C. H. (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.
Loh, X. K., Lee, V. H., Loh, X. M., Tan, G. W., Ooi, K. B., & Dwivedi, Y. K. (2022). The Dark Side of Mobile Learning via Social Media: How Bad Can It Get? Information Systems Frontiers, 1-18. https://doi.org/10.1007/s10796-021-10202-z
Lu, Y., Wang, B., & Lu, Y. (2019). Understanding key drivers of MOOC satisfaction and continuance intention to use. Journal of Electronic Commerce Research, 20 (2).
MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological methods, 1 (2), 130.
Maryanto, R. H., & Kaihatu, T. S. (2021). Customer loyalty as an impact of perceived usefulness to grab users, mediated by customer satisfaction and moderated by perceived ease of use. Binus Business Review, 12 (1), 31-39.
Navarro, D. J., & Foxcroft, D. R. (2019). Learning statistics with jamovi: A tutorial for psychology students and other beginners (Version 0.70). Tillgänglig online:. https://doi.org/http://learnstatswithjamovi.com
Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of marketing research, 17 (4), 460-469.
Patterson, P. G., Johnson, L. W., & Spreng, R. A. (1997). Modeling the determinants of customer satisfaction for business-to-business professional services. Journal of the Academy of Marketing Science, 25 (1), 4-17. https://doi.org/10.1007/BF02894505
Peterson, R. A., & Brown, S. P. (2005). On the use of beta coefficients in meta-analysis. Journal of applied psychology, 90 (1), 175.
Shin, D. H., Shin, Y. J., Choo, H., & Khisu, B. (2011). Smartphones as smart pedagogical tools: Implications for smartphones as u-learning devices. Computers in Human Behavior, 27 (6), 2207-2214. https://doi.org/https://doi.org/10.1016/j.chb.2011.06.017
Tobin, K. (1998). Qualitative Perceptions of Learning Environments on the World Wide Web. Learning Environments Research, 1 (2), 139-162. https://doi.org/10.1023/A:10099 53715583
Trochim, W., & Donnelly, J. P. (2006). The research methods knowledge base (3. bs.). Cincinnati, OH: Atomic Dog.
Tse, D. K., & Wilton, P. C. (1988). Models of consumer satisfaction formation: An extension. Journal of marketing research, 25 (2), 204-212.
Wang, Y. Y., Wang, Y. S., Lin, H. H., & Tsai, T. H. (2018). Developing and validating a model for assessing paid mobile learning app success. Interactive Learning Environments, 27 (4), 458-477. https://doi.org/10.1080/10494820.2018.1484773
Yahaya, W. A. J. W., & Zaini, K. M. (2020). The Effects of a Mobile App with Tutorial Learning Strategy on Anxiety Level of Secondary Students. TechTrends, 64 (3), 525-532. https://doi.org/10.1007/s11528-020-00505-4
Yeap, J. A., Ramayah, T., & Soto-Acosta, P. (2016). Factors propelling the adoption of m-learning among students in higher education. Electronic Markets, 26, 323-338.
Zaiţ, A., & Bertea, P. E. (2011). Methods for testing discriminant validity. Management Marketing Journal, 9 (2), 217-224.
Zheng, Y., Zhao, K., & Stylianou, A. (2013). The impacts of information quality and system quality on users' continuance intention in information-exchange virtual communities: An empirical investigation. Decision Support Systems, 56, 513-524. https://doi.org/10.1016/j.dss.2012.11.008