Multiple representations framework in technology acceptance: A structural equation modeling of science educational videos in teaching and learning redox reactions

Abstract

This study explored the role of the Multiple Representations Framework (MR) within the Technology Acceptance Model (TAM) in understanding pre-service teachers' acceptance of science educational videos in teaching and learning redox reactions. Conducted within the context of an analytical chemistry course, the study employed partial least squares structural equation modeling (PLS-SEM) to analyze the relationships between perceived ease of use (PEU), perceived usefulness (PU), and intention to use (IU), integrating motivation as a key variable. Descriptive statistics revealed positive perspectives on macroscopic, microscopic, and symbolic representations, as well as favorable views on TAM constructs. The measurement model confirmed the reliability and validity of the instruments, with strong outer loadings, internal consistency, and discriminant validity. MR significantly impacted PU and mediated the relationships between PEU, PU, and IU, highlighting its central role in enhancing perceived usefulness. However, MR did not moderate the relationship between PEU and PU, and PEU alone did not directly influence IU. These findings underscore the significance of content quality and representation in influencing technology adoption. The study concludes that integrating MR into educational tools improves conceptual understanding and supports technology adoption. It recommends incorporating MR-based resources into STEM curricula and providing professional development for educators to optimize their design and application.

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