Application of Deep Learning to Improve Mathematical Literacy in HOTS at MTs Al-Ihsan

Abstract

This study aims to analyze the implementation of the deep learning approach in improving students’ mathematical literacy in solving Higher Order Thinking Skills (HOTS) problems at MTs Al-Ihsan, Jombang. The research employed a quasi-experimental design with a pretest–posttest control group model. The subjects consisted of two classes, VII B as the experimental group and VII C as the control group, each comprising 27 students. The experimental class received mathematics instruction using a deep learning approach that integrated the principles of mindful, meaningful, and joyful learning, while the control class was taught using conventional methods. Data were collected through validated pretest and posttest instruments and analyzed using SPSS. The results of the normality and homogeneity tests showed that the data met the assumptions for parametric analysis (Sig. > 0.05). The independent sample t-test revealed a significant difference between the posttest results of the control and experimental groups (Sig. = 0.000 < 0.05). The N- Gain analysis showed that the experimental class achieved an average score of 0.76 (76.08%), categorized as high, while the control class obtained 0.43 (43.23%), categorized as medium. These findings indicate that the deep learning approach effectively enhances students’ mathematical literacy, particularly in solving HOTS-based problems.