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Data-Driven Strategies for Enhancing Student Success in Underserved U.S. Communities

David Iyanuoluwa Ajiga, Oladimeji Hamza, Adeoluwa Eweje, Eseoghene Kokogho, Princess Eloho Odio .

Abstract

This paper explores data-driven strategies to enhance student success in underserved communities across the United States. It addresses the significant educational disparities in rural and urban settings, focusing on the unique socioeconomic and cultural challenges students face. Through the use of predictive analytics, the research identifies at-risk students and proposes personalized interventions that can improve retention and graduation rates. The importance of community engagement and collaboration among local stakeholders is emphasized, highlighting the need for culturally responsive practices in education. Additionally, the paper aligns its findings with U.S. federal education priorities, advocating for policies that support the integration of data analytics into local and federal education strategies. Ultimately, this research aims to provide actionable recommendations for educators and policymakers to close the achievement gap and ensure equitable educational opportunities for all students.

Keywords

Data-driven strategiesPredictive analyticsStudent successUnderserved

References

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