Pre-Service Science Teacher Acceptance of and Satisfaction with Online Educational Classes through the Technology Acceptance Model (TAM)

Abstract

This study aims to examine pre-service science teachers’ acceptance of and satisfaction with online educational classes using the Technology Acceptance Model (TAM). A quantitative cross-sectional survey design was employed, involving 193 pre-service science teachers selected through purposive sampling. Data were collected using a structured questionnaire measuring four latent constructs: Perceived Ease of Use (PEU), Perceived Usefulness (PU), Satisfaction (SAT), and Acceptance Intention (AI). The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to evaluate both measurement and structural models. The results indicate that all constructs meet the criteria for reliability and validity, including convergent and discriminant validity. Structurally, PEU significantly influences PU and SAT, while PU significantly affects SAT and AI. Satisfaction emerges as the strongest predictor of acceptance intention. However, PEU does not directly influence AI, although it shows significant indirect effects through PU and SAT. These findings highlight the importance of usability, perceived benefits, and satisfaction in shaping students’ acceptance of online learning environments. The study provides insights for improving online educational practices in teacher education programs.