This research is motivated by junior high school students' suboptimal numeracy ability in understanding information, building mathematical models, connecting representations, applying concepts, reasoning, and examining and reflecting on results in straight-line equation materials. This research aims to develop an integrated teaching module, Growth Mindset and Deep Learning, that is oriented to the numeracy skills of junior high school students. The methods used are research and development with a 4D model, which includes define, design, develop, and disseminate. The define stage identifies teaching material needs that are contextual, adaptive, and student-centered. The data analysis techniques used in this study were tailored to the type of data and the research phases. During the define phase, the data were analyzed qualitatively using ATLAS.ti. Meanwhile, in the develop stage, the data were analyzed quantitatively, including a chi-square goodness-of-fit test for product validation, an analysis of the proportion of student and teacher responses to measure practicality, and a one-sample comparative test for target proportion criteria in measuring the product’s effectiveness. The development phase showed that the module met the validity criteria across three validators: media, pedagogy, and content. The module also demonstrated its practicality by receiving positive feedback from 76% of 30 students and 100% of observers regarding the implementation of the learning process. The effectiveness test showed that 28 out of 30 students (93.33%) fell into numeracy ability category 1, indicating a significant relationship between the KKTP criteria and students’ numeracy abilities. The dissemination stage was carried out by publishing modules on the PMT UNWIR page. Overall, the modules developed can serve as alternative teaching materials to support contextual, active, reflective, and meaningful mathematics learning, as well as strengthen students' numeracy skills and growth mindset.
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