Chien-Hua Shen's research while affiliated with Taiwan Food And Drug Administration and other places

This study aimed to explore the correlation among AR-based learning effectiveness (ARLE), AR learning beliefs (ARLB), creative learning self-efficacy (CLS), and AR learning motivation (ARLM) of tertiary students in Taiwan. Participants had 378 tertiary students who from 15 school, and used path analysis model of the structural equation model (SEM) based on the questionnaire survey. The results show that there is a significant positive correlation between the AR Learning Beliefs (ARLB) and AR Learning Motivation (ARLM) of tertiary student. It is found that higher correlation between ARLB, ARLM and Creative Learning Self-Efficacy (CLS). There was a significant positive correlation between ARLE and ARLB, and can effectively explain ARLE. This study confirms that CLS is an important factor and an impact mediating factor between ARLB and ARLM. In addition, CLS can also influence ARLE through ARLM.

The purpose of this study is to develop the ‘Teachers’ Efficacy Perceptions of the AI-based Teaching Applications (TEP-AITA) scale’ and to describe the analysis of the background variables and the differences between six factors in the application of an Artificial Intelligence (AI) teaching efficacy perceptions questionnaire. In total, 714 vocational senior high school teachers participated in random and cluster sampling in Taiwan. The results showed that the internal consistency reliability of the measure was .988 and that the reliability of the six subscales was .975, .971, .981, .976, .972, and .967. The results found that TEP-AITA scale included ‘Resource support’, ‘Innovative teaching’, ‘Cross-disciplinary’, ‘Professional learning’, ‘Learner demand’, and ‘Self-reflection’. ‘Cross-disciplinary’ and ‘Learner demand’ were highly rated, while ’Resource support’ was low. This study analyzed the differences in the perceived efficacy for AI-based teaching applications among teachers according to their background variables, such as their gender, current position, school attributes, teachers’ seniority, and application AI teaching experience. The research results on teacher background factors and the six factors of the TEP-AITA scale could be provided to education units for the active promotion of AI information technology teaching and training.

This research investigated 1,552 university students to explore the correlation between their learning effectiveness of artificial intelligence (AI) technology application and its influencing factors. The aim is to provide a reference for school planning and application of AI in information and communications technology (ICT) teaching. The results show that ICT self-efficacy (ICT-SE) has a significant direct effect, and human-computer interaction experience (HCIE) has a significant indirect effect on lLearning effectiveness of AI-based technology applications (LE-AITA). The impact model of university student ICT-SE and HCIE on LE-AITA exhibits a good fit. The recommendation is that Taiwan education improve the AI learning environment, provide a suitable platform for the use and development of educational technology, and create a seamless teaching and learning experience. We discuss influencing factors and provide suggestions for the development of AI education.

Mobile learning (M-learning) as a technology teaching has significant potential to improve student application and comprehension skills. The rapid development of the Internet and the resulting trends applying information available on the Internet have changed the nature of learning and learning behavior patterns of tertiary students. However, insufficient theoretical and empirical research on the effect of M-learning on learning attitude and M-learning effectiveness has been conducted for achieving any reliable understanding of the use of M-learning by tertiary students. This, study was therefore based on the theory of planned behavior, and combined the technology acceptance model and the structural equation model (SEM); it involved 892 tertiary student participants, and developed an empirical research model. The study found that in terms of Mlearning acceptance, tertiary students have a positive evaluation and perception of using M-learning, with perceived enjoyment being the most significant factor. In addition, the impact of perceived innovation was significant in helping teachers understand students’ learning outcomes. The teaching material of learning motivations was also significant, indicating that current tertiary students are more likely to value their friends as sources of information. External influences on self-efficacy were also significant, suggesting that teachers must consider the operability of M-learning technology, with particular attention paid to students’ competency with the technology itself, as difficulty of use will reduce students’ willingness to use M-learning.

The aim of this study was to analyze the correlation among teachers’ perceived acceptance of technological innovation, organisations’ innovation climate, and innovative teaching using information and communication technology (ICT) in Taiwan. The participants (N = 482) responded to a questionnaire using a five-point Likert scale for each factor, and their responses were analyzed using structural equation modeling (SEM). This research found that an organisation’s innovation climate is significantly and positively related with innovative teaching using ICT, and that the acceptance of technological innovation is significantly and positively related with innovative teaching using ICT, as supported by the organisation’s innovation climate. The influence pattern and empirical data of the acceptance of technological innovation and the organisation’s innovation climate on innovative teaching with ICT exhibited a good fit. The implications of such findings were discussed.