|
Factors Influencing the Use of Ubiquitous Learning in Higher Education in Sichuan, China in the Aftermath of Covid-19 Pandemic |
|---|---|
| รหัสดีโอไอ | |
| Creator | Meizi Huang |
| Title | Factors Influencing the Use of Ubiquitous Learning in Higher Education in Sichuan, China in the Aftermath of Covid-19 Pandemic |
| Publisher | Graduate School of Business and Advanced Technology Management Assumption University Thailand |
| Publication Year | 2564 |
| Journal Title | ABAC ODI Journal Vision. Action. Outcome |
| Journal Vol. | 9 |
| Journal No. | 1 |
| Page no. | 74-90 |
| Keyword | ubiquitous learning , performance expectancy, effort expentancy,social influence, facilitating conditions |
| URL Website | http://www.assumptionjournal.au.edu/index.php/odijournal |
| Website title | ABAC ODI Journal Vision. Action.Outcome |
| ISSN | 2408-2058 Online ISSN 2351-0617 Print ISSN |
| Abstract | This research aims to investigate factors for adoption of ubiquitous learning (u-learning) in higher education in China in the wake of the COVID-19 pandemic. Literature and theoretical models for adoption of ubiquitous learning were examined to find the key factors that would influence ubiquitous learning adoption which include performance expectancy, effort expectancy, social influence, facilitating conditions, intention to use and actual use. The research uses a quantitative, survey-based research design, employing online data collection. The study applied multistage sampling. First, a non-probability sampling method, judgmental sampling was used to draw a population of Chinese higher education students in Sichuan, China at three institutions: Sichuan Normal University Fine Arts College, Sichuan University of Arts and Sciences Academy of Art and Design, and Dazhou Vocational and Technical College Art Department. Second, stratified random sampling was applied to calculate the number of students to represent each program. Lastly, a sample size of 420 was determined based on the ratio of the number of students in each institution to the total number of populations, were selected through convenience sampling. For analysis of data, Confirmation Factor Analysis (CFA) and structural equation modeling (SEM) were utilized. The analysis showed that intention to use has the strongest effect on actual system use. Furthermore, effort expectancy, facilitating conditions, and social influence except performance expectancy were found to positively affect the intention to use u-learning. Hence, policymakers, universities executives, and educators are recommended to consider these factors to ensure technology adoption success. |
