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

Joje Mar P. Sanchez; Joje Mar P. Sanchez

doi:10.3934/steme.2025038

STEM Education

2025, Volume 5, Issue 5: 855-881. doi: 10.3934/steme.2025038

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Research article

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

Joje Mar P. Sanchez ^,

Science Education Department, College of Teacher Education, Cebu Normal University, Osmeña Boulevard, Cebu City, Philippines

Academic Editor: Tasadduq Imam

Received: 09 January 2025 Revised: 04 June 2025 Accepted: 16 July 2025 Published: 23 July 2025

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.
- multiple representations,
- redox reactions,
- science educational videos,
- technology acceptance
Citation: Joje Mar P. Sanchez. Multiple representations framework in technology acceptance: A structural equation modeling of science educational videos in teaching and learning redox reactions[J]. STEM Education, 2025, 5(5): 855-881. doi: 10.3934/steme.2025038

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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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Author's biography Dr. Joje Mar P. Sanchez earned his Science Education doctorate at Cebu Normal University (CNU). He is currently a faculty member and the doctorate program Chair of the College of Teacher Education at the same university. Dr. Sanchez has published research and expertise in chemistry/physics education, environmental education, educational data mining, and science investigatory project instruction. He is a member of the State Universities and Colleges Teacher Educators Association (SUCTEA), the Philippine Association for Teacher Education (PAFTE), and the Philippine Association of Chemistry Teachers (PACT)

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