Enhancing Educational Outcomes through Data-Driven Decision Making in K–12 Schools

Abstract

This empirical study investigates the impact of data-driven decision making (DDDM) on educational outcomes in K-12 schools across diverse socioeconomic contexts. Analysis of data from 42 schools implementing structured DDDM protocols revealed significant improvements in student achievement metrics, particularly in mathematics (11.3% increase) and reading comprehension (8.7% increase). Schools with comprehensive teacher training in data interpretation demonstrated more substantial gains compared to those with limited training. Implementation challenges identified include data literacy gaps, time constraints, and technology infrastructure limitations. Using a mixed-methods approach combining quantitative student performance data with qualitative assessments of implementation fidelity, this research provides evidence that systematic DDDM practices positively influence educational outcomes when adequately supported by professional development, administrative commitment, and appropriate technological infrastructure. The findings suggest that targeted investments in data systems and staff capacity building can foster more equitable and effective educational environments, particularly benefiting traditionally underperforming student populations.