Enhancing mathematics achievement in underserved high schools through data-driven instructional strategies: A case study approach

This paper discusses data-driven instructional practices to enhance mathematics performance in disadvantaged high schools. The study employs the mixed-methods case study design to determine how data collection, analysis, and instructional adaptation can help resolve long-standing achievement gaps in resource-scarce educational settings. The research is based on three urban high schools with the majority of students being low-income and historically marginalized. It explores the role of formative assessment, real-time instructional changes, and collaborative teacher practices based on student performance data in better academic performance. The standardized test scores are used to analyze quantitative data and to gather qualitative information through interviews and observation of the teachers to give a complete picture of the effectiveness of data-driven instruction (DDI). The results indicate a high level of improvements in student outcomes and engagement, as well as some implementation obstacles that have been revealed, including the low level of teacher training, infrastructure limitations, and data literacy issues. The research provides effective guidelines on how DDI can be scaled in other underserved settings by investing in the strategy and supporting it with long-term professional assistance.