Influence of School Management Data-Driven Practices on School Performance in Public Primary Schools in Gatsibo District, Rwanda

Abstract

This study investigated the influence of school management data-driven practices (DDP) on school performance in public primary schools in Gatsibo District, Rwanda. Guided by the Data-Informed Decision-Making Theory and the Theory of Performance, it examined the influence of four DDP components data collection, data analysis, data visualization, and actionable insights on student retention, academic achievement, and teacher engagement. A mixed-methods design integrated structured questionnaires, semi-structured interviews, and document analysis of school records. From a target population of 149 headteachers, 3,161 teachers, 14 sector education inspectors, and one district education officer, a sample of 109 headteachers (census), 366 teachers, 13 inspectors, and the officer (purposive) was drawn, achieving a 100% response rate. Quantitative data were analyzed in SPSS using descriptive statistics, Pearson correlation, and linear and multiple regression, while qualitative data were thematically analyzed in NVivo. The findings revealed a fractured DDP cycle. Data collection was widespread (81.6% of headteachers systematically collected attendance data) yet weakly significant as a predictor of retention (r = .218, R² = .048, p = .022). Data analysis was the most underdeveloped stage with 77.1% of teachers untrained and did not significantly predict academic achievement (R² = .026, p = .092). Data visualization was virtually absent (78.0–80.3% of respondents disagreed they used charts or dashboards) but significantly predicted teacher engagement where attempted (r = .345, R² = .119, p < .001). In a multiple regression of all four components on retention (R² = .137, p = .004), only actionable insights made a unique significant contribution (β = .312, p = .011). The study concludes that the DDP cycle is constrained less by a lack of data than by limited data literacy and technological infrastructure, and recommends investment in digital tools and reliable connectivity alongside sustained, school-based professional development in data literacy and evidence-based intervention design.