<![CDATA[The diversity of student characteristics requires an understanding of readiness, learning styles, interests, and learning environment preferences before instruction is designed, while much of the research on diagnostic assessment still focuses on a single domain. This study aimed to describe the implementation of cognitive and noncognitive diagnostic assessments and analyze their benefits as a basis for mapping students’ learning needs. The study employed a descriptive qualitative approach with a case study design involving 18 fifth-grade students at SDN Banyuajuh 2 in IPAS lessons on ecosystems and the diversity of flora and fauna. Data were collected through a cognitive diagnostic test and a Likert-scale noncognitive questionnaire covering learning styles, learning interests, learning readiness, and learning environment preferences. Data were analyzed descriptively using percentages and then synthesized into individual profiles and learning groups. The findings showed a mean class cognitive score of 66.1, with 22.2% of students in the high category, 55.6% in the moderate category, and 22.2% in the low category. A visual learning preference was identified in 72.2% of students, while 94.4% had moderate to high learning interest and 94.4% were classified as fairly ready or ready to learn. The integration of cognitive and noncognitive data identified one student with multilayered needs across the domains of cognition, interest, and learning readiness. These findings indicate that combining both types of assessment supports a more comprehensive mapping of learning needs by considering the interconnections among domains for each student. The practical implication is that teachers need to use the mapping results to adjust instructional content, processes, and products and evaluate these adjustments periodically.]]>
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