Developing an Extended Technology Acceptance Model (TAM) for Mathematics Learning: A Structural Equation Modeling Approach
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Abstract
The increasing integration of digital and AI-supported technologies in mathematics education has highlighted the need to understand how technology quality influences students’ learning experiences and outcomes. This study proposes and tests a parsi-monious extended Technology Acceptance Model–inspired framework to examine the relationships among technology quality perceptions, learning processes, and learning outcomes in technology-supported mathematics learning. A quantitative, cross-sectional survey design was employed, and data were collected from undergrad-uate students with experience using digital tools for mathematics learning. Structural equation modeling (SEM) was applied to analyze the data. The results revealed that technology quality perceptions—including feedback quality, adaptability, and clarity of mathematical content—had a significant positive effect on learning processes (β = 0.62, p < .001). In turn, learning processes exerted a strong posi-tive influence on learning outcomes, measured by mathematics learning achievement and mathematical literacy (β = 0.68, p < .001). Mediation analysis further indicated that learning processes fully mediated the relationship between technology quality percep-tions and learning outcomes, with a significant indirect effect (β = 0.42, p < .001). These findings suggest that the educational value of technology lies not only in its features but in its capacity to foster meaningful learning processes. This study contributes em-pirical evidence for a concise, learning-centered model explaining how perceived technology quality translates into effective mathematics learning.
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