Cultivating mathematical literacy and disposition through the synergy of problem-based learning and deep learning pedagogy

Purpose: This study explores how the combination of Problem-Based Learning and Deep Learning pedagogy influences students’ mathematical literacy and disposition, focusing on how both can nurture deeper understanding and sustained engagement. Method: A quasi-experimental approach using a Nonequivalent Pretest–Posttest Control Group design was implemented with 61 participants from MA Mathlaul Anwar Kedondong. The experimental group experienced learning through Problem-Based Learning with Deep Learning principles, while the control group followed conventional instruction. Data from literacy tests, disposition questionnaires, observations, and documentation were analyzed using normality, homogeneity, independent t-tests, and Mann–Whitney procedures. Findings: The integration of the two pedagogical models had a significant impact on mathematical literacy (p = 0.000 < 0.05), reflected in an average N-Gain of 0.57 for the experimental class compared to 0.21 in the control group. Meanwhile, no significant difference was found in students’ mathematical disposition (p = 0.194), although learners in the experimental class demonstrated more stable and constructive attitudes toward mathematics. Significance: Blending Problem-Based Learning with Deep Learning pedagogy effectively enhances mathematical literacy while gradually shaping students’ positive learning attitudes. Continuous and consistent use of this integrative model holds potential for strengthening mathematical disposition in the long term.