Learning Trajectories in Central Tendency Using Climate Change Context to Enhance Prospective Teachers’ Statistical Literacy - Drajat Friansah; Ratu Ilma Indra Putri; Yusuf Hartono; Somakim Somakim (Mathematics Education Journal, 2026)
Statistical literacy is a crucial competence for prospective mathematics teachers, enabling them to interpret, evaluate, and communicate data effectively in addressing real-world issues such as climate change. This study investigates how a structured learning trajectory on measures of central tendency, designed within climate change contexts, can foster both statistical literacy and sociomathematical norms that shape classroom discourse. Employing a qualitative design research approach, the study was implemented in two phases, a pilot and a teaching experiment, involving third-semester students in mathematics education. Data were collected through observations, student worksheets, interviews, and video recordings, and analysed retrospectively. Findings indicate that contextualized tasks using climate-related data, including temperature fluctuations and rainfall trends, enhanced students’ conceptual understanding, interpretation ability, and critical evaluation of statistical information. Importantly, the emergence of sociomathematical norms, such as justification of statistical choices, critique of peers’ reasoning, and negotiation of representational adequacy, played a key role in supporting collaborative meaning-making and refining statistical arguments. The study concludes that climate-integrated learning trajectories, combined with deliberate attention to sociomathematical norms, provide a robust pedagogical model. This integration equips future teachers with the skills to facilitate data-driven, socially negotiated, and context-rich statistical literacy, thereby contributing to teacher education and curriculum development.