<![CDATA[This research investigates the integration of deep learning principles with cognitive theories to facilitate data processing instruction among elementary school pupils. Primarily, a descriptive qualitative methodology was used, supplemented by quantitative evidence. The study involved 20 fifth-grade students across three intensive instructional sessions. Instruments for data gathering comprised pre- and post-assessments, direct observation of participants, semi-structured interviews, and records of classroom proceedings. Mean scores improved markedly from 58.4 to 82.6, signifying substantial advancement in conceptual mastery. Moreover, participants' learning behaviors transformed notably: they now engage actively, formulate reasoned arguments, and adeptly discern data insights beyond rote computation. Observations in the classroom illustrated students' progression from difficulty differentiating raw numerical values from their meanings to linking them to practical contexts, for instance, estimating household shopping expenditures. The scaffolded instructional framework, which connects theoretical abstractions to everyday activities through experiential exercises, demonstrated efficacy in enhancing long-term conceptual retention. Empirically, this method underscores deep learning's role as a vital conduit from mechanical repetition to genuine comprehension in foundational mathematics. This pedagogical strategy holds immediate relevance and necessitates widespread adoption to rectify curriculum deficiencies rooted in procedural superficiality, particularly in under-resourced educational settings.]]>
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