<![CDATA[This study aims to develop and validate an adaptive digital assessment system for identifying students’ learning styles based on sensory preferences to enhance personalized mathematics instruction at the secondary school level. Using a Research and Development approach with the ADDIE model, the digital instrument was designed, tested, and refined in three public junior high schools with varying levels of technological infrastructure. The digital assessment demonstrated high validity and reliability, efficiently mapping students’ sensory-based learning profiles in real-time. The system provides automated instructional recommendations that are easily adopted by teachers. Field implementation involved 128 students and 6 mathematics teachers, showing that 81.2% of students completed the assessment independently and 92% found it helpful in understanding their learning preferences. The distribution revealed dominant visual (42.2%) and kinesthetic (24.2%) styles, with a significant proportion of multi-sensory learners (11.7%). The system outperformed conventional and comparable digital tools in terms of sensitivity, efficiency, and effectiveness in supporting personalized learning. Implementation challenges related to infrastructure and digital literacy were observed. This study provides strong empirical evidence for the practical contribution of adaptive digital assessment in advancing data-driven, student-centered mathematics education.]]>
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