Abstract

Purpose: The purpose of this study is to examine the relationships among authentic learning and real-world application, learning self-efficacy, epistemic curiosity, intrinsic learning goal orientation, and student statistical reasoning in biostatistics education. The study specifically explores how authentic learning and learning self-efficacy contribute to the development of epistemic curiosity, how intrinsic learning goal orientation shapes these relationships, and how epistemic curiosity explains the link between instructional and motivational factors and student statistical reasoning. The use of open datasets is emphasized as a contextual foundation for promoting research literacy. Method: A quantitative, cross-sectional research design was employed. Data were collected from biostatistics students using established and validated measurement scales adapted from prior studies. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to test the proposed research model, including direct relationships, moderating effects, and mediation pathways among the study variables. Findings: The results indicate that authentic learning and real-world application, as well as learning self-efficacy, positively influence epistemic curiosity. Intrinsic learning goal orientation strengthens the relationships between both authentic learning and epistemic curiosity and between learning self-efficacy and epistemic curiosity. Furthermore, epistemic curiosity mediates the effects of authentic learning and learning self-efficacy on student statistical reasoning, highlighting its central role in translating instructional design and motivational beliefs into higher-order reasoning outcomes. Originality/Implications: This study presents an integrated motivational instructional framework for biostatistics education, demonstrating how epistemic curiosity connects authentic learning and self-efficacy to statistical reasoning. The findings offer practical guidance for designing learner-centered, data-driven biostatistics curricula that leverage open datasets to enhance research literacy.