Data-Driven Instructional Practices in Mathematics: The Role of Teacher Capacity and Institutional Support in Student Achievement in Nyarugenge District, Rwanda
David Niyitegeka, Martin Wanjala & Dickson Owiti
Masinde Muliro University of Science and Technology, Kenya
Email: davidniy93@gmail.com
Abstract: This study examined the relationships among data-driven instructional practices, teacher capacity, institutional support, and perceived student achievement in mathematics in public secondary schools in Nyarugenge District, Rwanda. The study was guided by Data-Driven Decision-Making (DDDM) Theory, the Theory of Planned Behavior (TPB), and the Technological Pedagogical Content Knowledge (TPACK) framework. A mixed-methods design was employed, involving 263 participants from 33 public secondary schools, including mathematics teachers, students, head teachers, and sector education officers. Data were collected using structured questionnaires and semi-structured interviews. The instruments demonstrated acceptable internal consistency, with Cronbach’s alpha coefficients of 0.756 for teacher data-use skills, 0.716 for institutional support, and 0.725 for perceived student achievement. Teachers reported high self-perceived competence in data use (M = 4.17–4.52), although gaps remained in translating data-use skills into individualized instructional support. Institutional support was generally positive, particularly in teacher training, while comparatively weaker areas included feedback mechanisms, collaborative opportunities, and resource provision. Students similarly reported that assessment data were more consistently used for general instructional planning and monitoring than for individualized support. The study concludes that effective data-driven mathematics instruction requires the integration of teacher capacity, practical application of data, and sustained institutional support. It recommends strengthening school leadership, structured feedback mechanisms, collaborative practices, and sustained professional development to enhance the effective use of student data in mathematics instruction.
