A PLS-SEM approach to model the motivation to learn mathematics in online classes: The mediating roles of self-reflection and self-monitoring

Learning mathematics is often perceived as demotivating by many students due to its varying levels of difficulty. Understanding the factors that influence student motivation in online learning is essential for informing educational management and policy decisions. To examine these relationships, this study employed Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze the links among parental encouragement, action planning, self-reflection, self-monitoring, and motivation to learn mathematics, including the mediating roles of self-reflection and self-monitoring. The dataset comprised responses from 1705 college students in the Visayas, Philippines, who voluntarily participated in the survey. Data were analyzed using SmartPLS software, with evaluation of measurement model reliability and validity, as well as structural model path coefficients and mediation effects. The results revealed a well-supported model with eight significant paths and four partial mediations. Parental encouragement and action planning positively influenced motivation to learn mathematics, with their effects partially mediated by self-reflection and self-monitoring. Self-monitoring emerged as a significant predictor of motivation to learn mathematics. The study provides practical implications for strengthening motivation and fostering self-regulated learning in online mathematics education.