Exploring student’s mathematical representation ability in deep learning-integrated problem-based learning on circle equations - Indhiadma Parnata; Khikmatus Shahrin Maghfiroh; Alifiani Alifiani; Lailatul Badriyah (JPMI (Jurnal Pembelajaran Matematika Inovatif), 2026)
Mathematical representation ability is a fundamental competency for senior high school students. This study aims to describe students' mathematical representation abilities in deep learning integrated with the Problem-Based Learning model. A descriptive qualitative approach was employed, involving 36 Grade XI students at SMAN 4 Malang. Data were collected through written tests based on mathematical representation ability indicators and analyzed across representation: verbal, pictorial, and symbolic. The results reveal uneven abilities across representation types: symbolic representation was most consistently fulfilled, though with varied strategies; pictorial representation was spatially constructed but incomplete in metric annotation; verbal representation exhibited the greatest variation, with only high-ability students capable of articulating contextual geometric conclusions. These findings confirm that symbolic proficiency does not guarantee comprehensive representational competence. This study contributes an empirical profile of students' mathematical representation in a PBL context, serving as a reference for teachers in designing balanced instruction across representation types.