Mathematical Modeling Competency of Pre-service Mathematics Teachers: Gap Diagnosis, Influencing Factors, and Cultivation Pathways

Abstract

Purpose: This study aims to identify gaps in the mathematical modeling competency of pre-service mathematics teachers, explore key influencing factors, and propose evidence-based cultivation pathways.

Methodology: A mixed-methods design was adopted. A questionnaire survey was administered to 305 pre-service mathematics teachers from six teacher education institutions in Sichuan Province, China, and semi-structured interviews were conducted with 21 teacher educators. Data were analyzed using the Modified Priority Needs Index (PNI), multiple regression analysis, and thematic analysis.

Main Findings: Based on evaluations by 67 teacher educators, the most critical competency gaps were algorithm implementation (PNI = 0.50), applicability analysis (PNI = 0.43), and interpretation of meaning (PNI = 0.34). Multiple regression analysis of the 305 pre-service teachers’ self-reports showed that critical thinking (β = 0.349, p < 0.001), practice opportunities (β = 0.225, p < 0.001), and evaluation orientation (β = 0.214, p < 0.001) were the strongest correlates of self-rated modeling competency among the variables examined, with the model accounting for 64.9% of the variance (adjusted R² = 0.649). Learning motivation, metacognition, and curriculum structure were also significantly associated with modeling competency (β = 0.110–0.158, p < 0.01).