Teacher Data Literacy and Data-Driven Mathematics Instruction in Primary Education - Vehachart, Rungchatchadaporn; Abdulwahab, Basel; Berngacha, Suhairee (Jurnal Genesis Indonesia, 2026)
This study investigates how primary school teachers develop and apply data literacy to support data-driven mathematics instruction in data-rich learning environments. A Systematic Literature Review (SLR) following PRISMA guidelines was conducted, synthesizing 42 peer-reviewed studies published between 2014 and 2025 across major academic databases. The review identifies three key themes. First, teacher data literacy is a multidimensional construct integrating data interpretation, statistical reasoning, and pedagogical decision-making, yet many teachers face challenges in translating data into instructional action. Second, barriers to data use are systemic, including limited training, complex analytics tools, time constraints, and weak institutional support. Third, effective strategies include structured data literacy training, simplified dashboards, collaborative inquiry through Professional Learning Communities (PLCs), and strong instructional leadership. This study integrates Data-Based Decision-Making (DBDM), Technological Pedagogical Content Knowledge (TPACK), and sociocultural perspectives into a unified framework, offering both theoretical insights and practical recommendations for developing sustainable data-driven mathematics teaching practices in primary education.