Teacher use of data-driven instructional practices and student achievement in mathematical concepts in Nyarugenge, Rwanda

David Niyitegeka; Martin Wanjala; Dickson Owiti

doi:10.51867/ajernet.7.4.18

Authors

David Niyitegeka Masinde Muliro University of Science and Technology, Kenya https://orcid.org/0009-0007-2482-3828
Martin Wanjala Masinde Muliro University of Science and Technology, Kenya https://orcid.org/0000-0002-8864-0234
Dickson Owiti Masinde Muliro University of Science and Technology, Kenya https://orcid.org/0000-0002-3940-1077

DOI:

https://doi.org/10.51867/ajernet.7.4.18

Keywords:

Data-driven instruction, mathematics achievement, teacher attitudes, data-use skills, institutional support, Nyarugenge District, Rwanda

Abstract

This study investigated teachers’ use of data-driven instructional practices and student achievement in mathematical concepts in public secondary schools in Nyarugenge District, Rwanda. It 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 combined quantitative analysis with qualitative inquiry into teacher experiences and institutional support. The study involved 263 participants: 23 mathematics teachers, 227 students, 11 head teachers, and 2 education officers. Data were collected through structured questionnaires and semi-structured interviews. Quantitative data were analyzed using descriptive statistics, independent-samples t-tests, and multiple regression at the 5% significance level; qualitative data were analyzed thematically. Pilot-test Cronbach’s alpha values were 0.84 for the teacher questionnaire and 0.87 for the student questionnaire. Findings indicated moderate application of data-driven practices: lesson-plan adjustment and collaboration were more common than individualized feedback and use of data dashboards. Teachers reported positive attitudes and high self-rated data-use skills, but qualitative evidence showed a gap between knowing what data indicate and deciding what instructional action to take. Institutional support was strongest in training provision and weaker in structured feedback and collaborative opportunities. The reported regression model indicated that the four predictors collectively explained 78.9% of the variance in student achievement. However, achievement was measured primarily through teacher-reported perceptions, limiting causal interpretation. The study recommends strengthening data-to-action protocols, individualized feedback, closed-loop verification, collaborative data-use structures, and pedagogically integrated professional development.

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