Miskonsepsi Matematis pada Soal Model PISA: Studi Kualitatif Ditinjau dari Tipe Kepribadian STIFIn
DOI:
https://doi.org/10.28918/circle.v6i2.2616Abstract
Mathematical literacy in PISA-model problems requires students to formulate, employ, and interpret mathematical concepts in various contexts. In practice, however, many students still demonstrate misconceptions that stem from differences in processing information and reasoning. This descriptive qualitative study aims to describe the variations in individual misconception patterns among seventh-grade students (Class VII D) at SMP Nuris Jember when solving PISA-model mathematics problems. The STIFIn personality typology framework (Sensing, Thinking, Intuiting, Feeling, Instinct) is utilized solely as a descriptive lens to contextualize the subjects, rather than as a causal variable or for population generalization. Nine subjects were selected purposively based on STIFIn test results to represent the five personality types and their subtypes. Data were gathered through a PISA-model test, task-based interviews, and documentation, then analyzed using technical triangulation by comparing written answers with verbal explanations. The findings illustrate specific variations across the investigated subjects: sensing subjects demonstrated strong oral articulation but lacked systematic written notations and procedures; thinking subjects showed relatively sound conceptual understanding but exhibited errors in chained computations and variable reduction within economic contexts; intuiting and instinct subjects showed a tendency to bypass formal procedural steps despite displaying evaluative reasoning; while feeling subjects consistently faced challenges with technical terms and mathematical symbols despite effectively conveying oral arguments. Because these findings represent single-case profiles for each investigated subtype, the observed patterns are not intended to generalize the characteristics of each STIFIn type. Instead, they provide preliminary insights for educators to better understand unique learning obstacles when designing more personalized remediation.
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