Self-Regulated Learning and Mathematical Modeling Competence Among Secondary School Students in Technology-Enhanced Classrooms

Abstract

Guided by the growing need to strengthen independent learning and real-world mathematical reasoning, this study determined the relationship between self-regulated learning and mathematical modeling competence among secondary school students in technology-enhanced classrooms at Isabela National High School in the City of Ilagan, Isabela. Using a quantitative descriptive-correlational design, the study involved students selected through simple random sampling. A validated survey questionnaire was used to measure self-regulated learning in terms of goal setting and planning, self-monitoring and strategy use, and self-reflection and adjustment, as well as mathematical modeling competence in terms of understanding and interpreting real-world problems, formulating mathematical representations, and analyzing and validating solutions in context. Descriptive statistics such as weighted mean and standard deviation were used to determine the levels of the two variables, while Pearson Product-Moment Correlation and multiple regression analysis were employed to test their relationship and predictive dimensions. Findings revealed that the respondents demonstrated high levels of self-regulated learning and mathematical modeling competence. A significant positive relationship was also found between the two variables, indicating that students with stronger self-regulatory behaviors tended to show higher competence in mathematical modeling. Among the dimensions of self-regulated learning, self-monitoring and strategy use emerged as the strongest predictor of mathematical modeling competence. The findings suggest that strengthening students’ self-regulatory capacities in technology-enhanced mathematics classrooms may contribute to improved performance in modeling-oriented tasks and more meaningful engagement in real-world mathematical problem solving.