Fuzzy-AHP for Teaching Quality Assessment and Student Performance Prediction in Mathematics Education Program
Abstract
This study proposes a fuzzy–Analytic Hierarchy Process (Fuzzy-AHP) model to evaluate teaching quality and predict student academic performance in a Mathematics Education program, based on data collected from 100 undergraduate Mathematics Education students (n = 100). A structured Evaluation Index System (EIS) comprising six criteria and twenty-six sub-criteria was constructed, with criterion weights derived using AHP based on expert judgments and student responses represented as triangular fuzzy numbers. The model produces composite teaching quality scores through fuzzy aggregation and centroid defuzzification, identifying Integration and Relevance of Teaching as the most influential dimension. Predictive validation using Spearman correlation and linear regression confirms a significant positive relationship between teaching quality and academic performance (ρ = 0.46, p < .01), with instructional quality explaining 21% of performance variance. From an applied mathematics perspective, this study contributes a formally structured fuzzy-AHP modelling framework with empirical predictive validation, advancing teaching quality assessment beyond descriptive ranking toward evidence-based performance prediction.
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DOI: http://dx.doi.org/10.30829/zero.v9i3.27041
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