Predictors of Perceived Satisfaction With The Moodle Lms Mediated By Instructor Quality In University Medical Students

The purpose of this study is to analyse the predictors of perceived satisfaction with the Moodle LMS among university medical students, considering the quality of information and personalisation as predictor variables, and the mediating role of instructor quality in the digital learning experience. The research is conducted using a quantitative approach, with a non-experimental, cross-sectional, and explanatory design. The sample consists of 208 human medicine students from a private university in southern Peru. Data collection is conducted via an online questionnaire with items on a five-point Likert scale. Data analysis is performed using partial least squares structural equation modelling (PLS-SEM), with the measurement and structural models evaluated sequentially. The results show that information quality has the strongest direct effect on perceived satisfaction and also significantly influences instructor quality, which is consolidated as a key predictor of student satisfaction. Likewise, LMS personalisation has moderate direct effects on satisfaction and significant indirect effects through instructor quality. The proposed model has high explanatory power, accounting for a substantial proportion of the variance in instructor quality and perceived satisfaction. It is concluded that satisfaction with the Moodle LMS depends not only on its technological characteristics but also on its alignment with the pedagogical quality of the instructor, underscoring the need to integrate the system's technological design with strategies to strengthen teaching in university medical education.