Reducing classification risk in developmental performance assessment using fuzzy logic: evidence from preservice mathematics teacher education

Performance assessment in professional education often relies on weighted score aggregation and rigid thresholds, even in developmental contexts where such approaches can increase misclassification risk and obscure emerging competence. This study investigates whether a fuzzy logic-based assessment framework can support more interpretable and defensible classification decisions than traditional statistical evaluation in preservice mathematics teacher education. Sixty-six preservice secondary mathematics teachers were randomly assigned to an experimental group assessed using fuzzy logic or a control group assessed using conventional weighted aggregation with cut scores. The fuzzy framework integrated lesson plan quality and microteaching performance to model instructional readiness without rigid thresholds and to represent graded membership in overlapping competence states. Compared with the Traditional Aggregation Model, fuzzy logic assessment produced greater differentiation across competence profiles, was associated with reduced compression around classification boundaries, and was supported by independent observation-based judgment. Longitudinal analyses across two semesters further suggested that the profile-oriented, differentiated feedback enabled by fuzzy classifications which distinguished between planning-side and enactment-side developmental needs was associated with stronger gains in both microteaching performance and lesson planning quality. These findings position fuzzy logic assessment as a promising applied measurement approach for developmental performance assessment in professional education, particularly where competence is multidimensional, uneven, and sensitive to threshold effects.