Design and Evaluation of a Guided Discovery Learning Model to Enhance Junior Secondary Students' Statistical Literacy

Aim. This study develops and evaluates the Guided Discovery Learning (GDL) model to enhance junior high school students’ statistical literacy. The model integrates contextual problem-solving tasks to strengthen students’ understanding of statistics while fostering statistical literacy. Methods. A mixed-methods approach with an explanatory sequential design was employed. The GDL model was developed through Educational Design Research (EDR) and subsequently evaluated using a one-group pre-test–post-test design. Quantitative data were analysed using the Wilcoxon Signed-Rank Test and effect size, while qualitative insights were derived from semi-structured interviews with students and teachers to explore their experiences during implementation. Results. Expert validation confirmed the strong alignment of the GDL design with the intended outcomes of statistical literacy, emphasising contextual relevance and scaffolding. Statistical analysis indicated significant improvement, with the median score rising from 50.00 to 87.00 (Z = -5.552, p < 0.001). All students improved, with none declining. The calculated effect size (r = 0.878) demonstrated a substantial positive impact of the intervention. Conclusions. This study addresses theoretical needs by advancing a constructivist-based framework, while meeting practical needs by offering an empirically tested model that enhances statistical literacy. The findings highlight the value of contextually driven, problem-based pedagogy in equipping students with critical, data-informed decision-making skills essential in the big data era. Originality. This study contributes a structured, inquiry-based GDL framework integrating authentic, real-world problems. It provides a discovery-oriented strategy that develops students’ reasoning and decision-making abilities aligned with contemporary educational priorities.