Deep learning for enhancing mathematical reasoning skills in vocational high school students - Rifdah Qultum Nada; Alvina Delicia Irawan (IKIP Siliwangi, 2026)
Mathematical reasoning ability is an essential competence in mathematics learning, as it enables students to analyse patterns, formulate conjectures, construct logical arguments, and draw systematic conclusions. However, studies indicate that this ability, particularly among vocational high school (SMK) students, remains relatively low due to learning practices that emphasize procedures and formula memorization rather than deep conceptual understanding. Therefore, an appropriate learning approach is needed to promote critical and reflective thinking. This study aims to examine the effectiveness of the Deep Learning approach in improving the mathematical reasoning abilities of tenth-grade vocational high school students. A quantitative approach with an experimental method and a pretest–post-test control group design was employed. The subjects consisted of two classes selected through cluster random sampling: an experimental class receiving the Deep Learning approach and a control class receiving conventional instruction. The instrument was a mathematical reasoning test administered before and after learning. Data were analysed using descriptive statistics, followed by normality, homogeneity, and t-tests. The results showed that the experimental class achieved a higher average post-test score, with a significance value of 0.000 < 0.05, indicating a significant difference. Thus, the Deep Learning approach is effective in improving students’ mathematical reasoning abilities