<![CDATA[Despite the growing implementation of Deep Learning-oriented instruction in mathematics education, empirical evidence remains limited regarding how its integration with mathematical recreation is reflected in students' achievement profiles and mathematical understanding in algebraic operations. This study aimed to examine students' achievement profiles following the implementation of a Deep Learning-based instructional module integrated with mathematical recreation. A descriptive quantitative design was employed involving 38 seventh-grade students at a junior secondary school. Data were collected through a post-instruction achievement test and analyses of students' written responses. The findings showed that students achieved a mean score of 81.00, with 81.6% meeting the Minimum Mastery Criterion, while achievement was predominantly concentrated in the medium-ability category. High-achieving students demonstrated stronger conceptual understanding and procedural fluency, whereas medium- and low-achieving students continued to experience difficulties in symbolic manipulation, identifying like terms, and applying operations involving negative signs. These findings indicate that the integration of Deep Learning principles with mathematical recreation was associated with generally satisfactory learning outcomes while revealing meaningful variation in students' mathematical understanding. Beyond reporting average achievement, this study demonstrates that achievement profiling combined with analyses of students' written responses provides richer pedagogical evidence for evaluating mathematics instruction and contributes to the growing literature on Deep Learning by offering a more comprehensive framework for understanding students' learning outcomes in algebra.]]>
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