Introduction. Technological innovations have introduced numerous changes to the education environment, providing scope for improved learning experiences and academic outcomes. The application of sophisticated tools, including artificial intelligence, virtual reality, and learning management systems in a classroom, can easily convert conventional teaching methods into dynamic and interactive ones. The purpose of this research is to examine the interplay of technological self-efficacy, chatbot acceptance, and task-technology fit, investigating their effects on students’ academic performance and learning outcomes. Additionally, the study explores the mediating role of technology use in learning and the moderating effects of chatbot acceptance and task-technology fit on the relationship between technological self-efficacy and academic performance.
Study participants and methods. A sample of 302 students from various Chinese schools participated in the study. Data were gathered using validated scales for technological self-efficacy, chatbot acceptance, task-technology fit, and learning outcomes. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to analyze the relationships between these constructs.
The results. Technological self-efficacy did not significantly influence academic performance directly (T = 1.011, p = 0.156). However, technology use in learning significantly mediated this relationship (β = 0.170, T = 2.140, p = 0.016). Both chatbot acceptance (β = 0.161, T = 1.92, p = 0.028) and task-technology fit (β = 0.090, T = 1.748, p = 0.041) were significant moderators, highlighting their critical role in enhancing technology adoption and learning outcomes.
Conclusions. This research highlights the significance of aligning technology with educational tasks and boosting students’ confidence in technology use. The findings shows that technological self-efficacy, chatbot acceptance, and task-technology fit, significantly effects students’ academic performance and learning outcomes. The findings offer valuable insights for educators and policymakers to design effective, technology-driven, student-centered learning environments.
Keywords: technological self-efficacy, use of technology in learning, chatbot acceptance, task technology fit, performance impact
For Citation: Bi, Y., & Hatta, Z. A. (2025). Bridging the digital divide: analyzing educational inequality in technology access between urban and rural schools in China. Perspektivy nauki i obrazovania = Perspectives of Science and Education, (5), 710–725. https://doi.org/10.32744/pse.2025.5.46
Authors:
Ying Bi (Petaling Jaya, Selangor, Malaysia) — PhD. Lincoln University College. E-mail: 18504646789@163.com
Zulkarnain Ahmad Hatta (Petaling Jaya, Selangor, Malaysia) — PhD, Professor. Lincoln University College. E-mail: zulkarnain@lincoln.edu.my
Оставить комментарий