This study aims to analyze learning needs and design Deep Learning-based teaching materials on the topic of Systems of Linear Equations in Two Variables (SLETV). The study employed a Research and Development (R&D) approach using the ADDIE model, limited to the analysis and design stages. The data sources were obtained through literature studies, including scientific journals, reference books, and previous studies related to Deep Learning, mathematics teaching materials, and SLETV topics. The data analysis technique used was descriptive qualitative analysis. The results showed that SLETV learning is still predominantly procedure-oriented, causing students difficulties in understanding concepts, modeling mathematical problems, and solving contextual problems. Based on these findings, Deep Learning-based teaching materials were designed by integrating the dimensions of meaningful, mindful, and joyful learning through contextual problem presentation, concept exploration activities, discussions, reflections, and problem-solving exercises. The teaching materials were systematically designed to include learning objectives, concept maps, learning materials, student activities, exercises, reflections, and evaluations. These teaching materials are expected to support more meaningful mathematics learning and help students understand SLETV concepts more deeply.
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