Enhancing Mathematical Representation and Reasoning Through Metacognitive Problem-Based Learning - Rahmat Hidayat; Rahmat Saleh (Journal of Mathematics Instruction, Social Research and Opinion, 2026)
Mathematical representation and reasoning are critical competencies that students often struggle to master. This study aims to evaluate the effectiveness of the Metacognitive Problem-Based Learning (MPBL) model in enhancing these abilities, while also examining the predictive roles of self-efficacy and metacognitive awareness. Using a quasi-experimental pretest-posttest non-equivalent control group design, a sample of 350 secondary students in Indonesia was selected via cluster random sampling. The experimental group (n=200) received MPBL, while the control group (n=150) followed conventional instruction. Results indicated that the MPBL group achieved significantly higher N-gains in mathematical representation (0.72, "High") and reasoning (0.68, "Medium") than the control group. Statistical analysis confirmed that the improvements were significant (p < 0.05). Furthermore, multiple regression analysis revealed that metacognition and self-efficacy jointly predicted 58% of the variance in problem-solving performance (p < 0.05). These findings suggest that integrating metacognitive scaffolding within problem-based frameworks is a potent strategy for fostering higher-order thinking in mathematics education.