The Selectivity and Suitability of Online Learning Resources as Predictor of the Effects of Self-Efficacy on Teacher Satisfaction During the COVID-19 Lockdown

The difference of primary school teachers' online teaching satisfaction in subject and educational level

The growth of online education requires high-quality online teaching. Teachers' satisfaction with online teaching is of great significance for improving online teaching effectiveness. This study was to explore the primary school teachers' online teaching satisfaction during the spread of COVID-19 from Shanghai, who have experienced online teaching, and explore whether there were differences of teachers' online teaching satisfaction in subject and educational level. 939 teachers from Shanghai participated in the study. The non-parametric Mann-Whitney U test and Kruskal-Wallis test of variance were performed. Results showed that teachers' online teaching satisfaction was at a high level. Moreover, there was a significant difference in teachers' subject and educational level on online teaching satisfaction. In terms of subject differences of teachers, there were significant differences in resource suitability (RS) among teachers of different subjects. Therefore, it is recommended that each subject should develop the online teaching resources to support teachers' online teaching. In terms of the differences in teachers' educational levels, there were no significant differences between the satisfaction of college-level teachers on non-technical variables such as content selectivity (CS) and teachers with undergraduate and graduate degrees, and only on technical variables such as self-efficacy (SE), resource suitability (RS), ease of use (EU), and intention to use (IU). Given the national context in China, the difference in educational levels may be more reflected in the age of the teachers. For teachers with college educational level, due to their older age, rather than simply enhancing motivation and improving learning ability to increase online teaching satisfaction, emphasis should be placed on providing appropriate teaching service support to help improve online teaching effectiveness. The findings provide new empirical evidence, strategies and Chinese experience for promoting teachers' online teaching satisfaction.

Exploring Factors of the Willingness to Accept AI-Assisted Learning Environments: An Empirical Investigation Based on the UTAUT Model and Perceived Risk Theory

Artificial intelligence (AI) technology has been widely applied in many fields. AI-assisted learning environments have been implemented in classrooms to facilitate the innovation of pedagogical models. However, college students' willingness to accept (WTA) AI-assisted learning environments has been ignored. Exploring the factors that influence college students' willingness to use AI can promote AI technology application in higher education. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and the theory of perceived risk, this study identified six factors that influence students' willingness to use AI to analyze their relationships with WTA AI-assisted learning environments. A model including six hypotheses was constructed to test the factors affecting students' WTA. The results indicated that college students showed "weak rejection" of the construction of AI-assisted learning environments. Effort expectancy (EE), performance expectancy (PE), and social influence (SI) were all positively related to college students' WTA AI-assisted learning environments. Psychological risk (PR) significantly negatively influenced students' WTA. The findings of this study will be helpful for carrying out risk communication, which can promote the construction of AI-assisted learning environments.