Exploring the Impact of ITEF on Sustainable Learning Motivation in the Context of Intelligent Manufacturing
Authors: Pan Dianwang1; Mao Yanyi1
Affiliations:
- 1Faculty of Education and Humanities, UNITAR International University, Petaling Jaya, Malaysia
Abstract
In the era of intelligent manufacturing, the education sector is undergoing a profound transformation that requires teachers to rethink how to stimulate and sustain students’ learning motivation. This study aims to explore the Interactive Transfer Expectation Framework (ITEF), a comprehensive model that integrates constructivist theory, learning transfer theory, and expectation theory, and examines its impact on the intrinsic drive for learning in the context of sustainable development. The ITEF framework emphasizes enhancing teacher–student interaction and peer-to-peer interaction, promoting knowledge transfer ability, and managing learners’ expectations to improve learning motivation. This study adopts a quantitative research approach, collecting data through questionnaire surveys to assess the influence of the interaction, transfer, and expectation dimensions of the ITEF framework on intrinsic learning drive. Descriptive statistics, correlation analysis, and regression analysis are used to examine the relationships among these dimensions and demonstrate the framework’s effectiveness in fostering intrinsic learning drive for sustainable development. The findings aim to provide valuable insights for educational reform in the context of intelligent manufacturing, helping students develop sustainable learning motivation and establish a strong foundation for their future careers.
Keywords: Intelligent Manufacturing; Internal Drive for Learning; Interactive Transfer Expectation Framework (ITEF); Sustainable Development
Suggested citation
Pan, D., & Mao, Y. (2025). Exploring the Impact of ITEF on Sustainable Learning Motivation in the Context of Intelligent Manufacturing. In TheSustainImpact International Forum 2025 Book of Abstracts (Vol. 1, Issue 1). TheSustainImpact. ISSN 3051-7362.