Sample Analysis of Double-Decrease Education Implementation relying on Fuzzy Cluster Analysis

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Chunhui Zhao, Chanjuan Zeng


This study explores the implementation of Double-Decrease Education Initiatives (DDEI) through the lens of Fuzzy Cluster Analysis (FCA), aiming to elucidate the multifaceted factors influencing educational outcomes. Leveraging a comprehensive dataset encompassing variables such as student demographics, socio-economic indicators, academic performance metrics, teacher characteristics, and institutional attributes, FCA identified distinct clusters representing diverse educational profiles. Analysis revealed five clusters: "High Achievers," "Striving Urban Schools," "Rural Excellence," "Suburban Stability," and "Underserved Urban Centers," each characterized by unique demographic compositions and academic outcomes. Statistical comparisons across clusters highlighted significant differences in key educational indicators, underscoring the impact of contextual factors on student achievement. The findings underscore the importance of tailored interventions to address the specific needs and challenges facing different educational contexts and inform evidence-based decision-making towards fostering equitable and inclusive learning environments. This study contributes to the ongoing discourse on educational reform and offers valuable insights for policymakers, educators, and stakeholders striving to improve educational equity and outcomes.

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