Peningkatan Resiliensi Matematis Siswa melalui Model Problem Based Learning Berpendekatan Deep Learning
DOI:
https://doi.org/10.31851/indiktika.v8i2.21803Keywords:
Mathematical Resilience, Problem Based Learning, Deep Learning, Mathematics LearningAbstract
Mathematical resilience is a crucial element in mathematics education as it embodies students' perseverance, self-assurance, adaptability, and readiness to confront hurdles in resolving mathematical problems. This study seeks to evaluate the enhancement of students' mathematical resilience via the use of the Problem Based Learning (PBL) paradigm utilising a Deep Learning technique. This study utilised a quasi-experimental design using a pretest-posttest nonequivalent group structure comprising three groups: direct instruction, PBL, and PBL with a Deep Learning technique. The participants comprised eleventh-grade students of SMA Negeri 1 Medang Deras chosen by purposive sampling. Data were gathered with a mathematical resilience questionnaire and analysed by descriptive statistics, N-Gain analysis, normality and homogeneity testing, ANOVA, and post hoc tests. The findings indicated that all learning models enhanced students' mathematical resilience; however, the PBL model utilising a Deep Learning technique yielded the most significant improvement relative to the other groups. The results demonstrate that the integration of Problem Based Learning with a Deep Learning methodology fosters greater persistence, reflection, collaboration, and confidence among students while confronting mathematical challenges. Consequently, the PBL model utilising a Deep Learning approach may be seen as a viable alternative for augmenting students' mathematical resilience in mathematics education.
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