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Optimizing Pedagogical Interventions and Advancing Student Performance Using Fuzzy C-Means Clustering
Abstract
Pedagogical interventions for enhancing students' academic performance need effective data-driven solutions to identify learning patterns and provide personalized assistance. In this study, an advanced Fuzzy C-Means (FCM) clustering technique is employed to group students based on academic performance, attendance, and learning behavior. Principal Component Analysis (PCA) is used for feature selection to maximize dimensionality reduction without losing crucial information. The improved FCM integrates adaptive membership functions and hybrid similarity measures to optimize clustering precision, while dynamic cluster optimization determines the number of student clusters. A fuzzy rule-based system translates clustering results into actionable recommendations for instructors to provide differentiated pedagogical interventions. The result substantiates that the proposed approach is capable of efficiently detecting distinctive learning profiles, which enables the implementation of accurate strategies such as advanced coursework, mentorship, and remedial programs.
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