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

Artigo2026

Hiba Naccache; Rima Hleiss

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Resumo

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.

Dados da obra

Tipo
Artigo
Periódico
Frontiers in Education, v. 11
Editora
Frontiers Media S.A.
Ano
2026 (2026-07)
Idioma
en
Palavras-chave
classification decisions; developmental assessment; educational measurement; fuzzy logic assessment; performance assessment; preservice mathematics teachers
Temas
Avaliação, Formação de Professores de Matemática
DOI
10.3389/feduc.2026.1840093
ISSN
2504-284X
Acesso
Acesso aberto
Fontes
DOAJ — Directory of Open Access Journals
Na Biblioteca desde
01/10/2026 · verificada em 03/10/2026

Como citar

ABNT
NACCACHE, Hiba; HLEISS, Rima. Reducing classification risk in developmental performance assessment using fuzzy logic: evidence from preservice mathematics teacher education. Frontiers in Education, v. 11, 2026. DOI: https://doi.org/10.3389/feduc.2026.1840093.

APA

Naccache, H., & Hleiss, R. (2026). Reducing classification risk in developmental performance assessment using fuzzy logic: evidence from preservice mathematics teacher education. Frontiers in Education, 11. https://doi.org/10.3389/feduc.2026.1840093

BibTeX
@article{naccache2026,
  author = {Naccache, Hiba and Hleiss, Rima},
  title = {Reducing classification risk in developmental performance assessment using fuzzy logic: evidence from preservice mathematics teacher education},
  year = {2026},
  journal = {Frontiers in Education},
  volume = {11},
  publisher = {Frontiers Media S.A.},
  doi = {10.3389/feduc.2026.1840093},
  language = {en}
}
RIS
TY  - JOUR
AU  - Naccache, Hiba
AU  - Hleiss, Rima
TI  - Reducing classification risk in developmental performance assessment using fuzzy logic: evidence from preservice mathematics teacher education
PY  - 2026
JO  - Frontiers in Education
VL  - 11
DO  - 10.3389/feduc.2026.1840093
LA  - en
ER  - 

Referência gerada automaticamente a partir dos metadados das fontes; confira com o documento original. Baixar BibTeX · Baixar RIS

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