Predictors of Statistical Literacy among Teacher-Education Graduate Students

Statistical literacy is critical for educators because it allows them to evaluate data effectively, make educated decisions, and create data-driven learning environments. In a world where data impacts educational policy and classroom practices, providing teachers with good statistical reasoning abilities ensures that they can critically evaluate study findings and use statistical principles effectively. The research evaluated teacher-education graduate students’ statistical literacy together with research literacy and AI literacy profiles for the academic year 2024-2025. The research used descriptive and inferential statistics for statistical literacy assessment while examining research literacy dimensions, including awareness, attitude, skills, and use, as well as AI literacy dimensions with affective, behavioral, cognitive, and ethical aspects, and determined substantial statistical literacy predictors. The research used descriptive-predictive methods for analyzing literacy levels and predicting important literacy through multiple linear regression. Graduate students within the Master of Arts in Teaching (MAT), Master of Arts in Education (MAED), and Master of Science in Teaching (MST) programs at Saint Mary’s University formed the respondents sample, which consisted of 42 students. The study findings demonstrate that students excel at descriptive statistics but struggle with inferential reasoning. Student performance on research literacy tests demonstrates average accuracy, but they require improvement in their ability to execute and share research, while AI literacy assessments reveal positive attitudes combined with challenges in advanced engagement along ethical duties. The results demonstrate no meaningful effect of research literacy or AI literacy on statistical literacy, which shows that basic numerical skills, together with teaching methods and educational planning, dominate statistical understanding development. The research demonstrates the vital importance of using active learning techniques with statistical software tools and real-world examples to overcome statistical thinking deficits. Programs that teach AI literacy within research training will give future educators better abilities for data-driven decision-making. New research directions should determine extra predictors alongside using sophisticated statistical modeling methods to optimize teacher education instruction.