Blended learning has gained prominence in higher education in response to the growing integration of technology in society, aiming to enhance student engagement by combining face-to-face instruction with online components. This pedagogical approach supports autonomy, encourages active participation, and promotes critical thinking by enabling students to apply theoretical concepts to practical situations through digital tools and interactive platforms.

This study investigates the key factors influencing student motivation and satisfaction in blended learning environments. It draws on two theoretical frameworks: Self-Determination Theory (SDT), which highlights the psychological needs of autonomy, relatedness, and competence as essential for intrinsic motivation; and the Technology Acceptance Model (TAM), which focuses on perceived usefulness (PU) and perceived ease of use (PEOU) as predictors of technology acceptance.

The research was conducted at a Portuguese university with a sample of 444 students, who completed a questionnaire comprising 38 items distributed across seven constructs (namely, Autonomy, Relatedness, Competence, PU, PEOU, Learning Motivation (LM), and Learning Satisfaction (LS)), alongside sociodemographic data. The analysis employed Partial Least Squares Structural Equation Modelling (PLS-SEM), evaluating both the measurement model and the structural model, using bootstrapping to assess the significance of the path coefficients.

The results confirmed most of the formulated hypotheses. Perceived ease of use and perceived usefulness were the strongest predictors of learning motivation, which, in turn, significantly influences learning satisfaction. Additionally, PU and PEOU mediated the relationship between the SDT factors and learning motivation. However, PEOU did not mediate the relationship between competence and PU.

These findings offer valuable insights for university leaders, educators, and student organisations, highlighting the importance of aligning blended learning strategies with students’ psychological and technological needs to enhance their motivation and overall satisfaction.

Keywords: Blended learning, University student, data analysis, PLS-SEM.