Crafting epistemic knowledge in statistics education via didactical engineering integrating the theory of didactical situations and the SECI model

Abstract Crafting epistemic knowledge in statistics education via didactical engineering integrating the Theory of Didactical Situations and the SECI model, this study theorizes and examines how the orchestration of TDS situations with SECI conversions structures students’ progression from tacit insights to justified explicit understanding. The objectives of this study are: (1) to explore whether the application of didactical engineering (DE) can produce epistemic knowledge in the context of statistics education, (2) to evaluate the effectiveness of integrating the Theory of Didactical Situations (TDS) and Nonaka’s SECI (Socialization, Externalization, Combination, Internalization) model in fostering students’ understanding of statistical concepts, and (3) to analyze the variations in students’ knowledge levels using cluster analysis. The research applies a didactical engineering approach, involving the design, implementation, observation, and a posteriori analysis of learning tasks related to statistics. Cluster analysis was employed to assess variations in student knowledge levels, allowing for targeted instructional design adjustments. The findings reveal that the integration of TDS and the SECI Model within didactical engineering significantly enhances students’ understanding of statistical concepts. Students’ knowledge evolved from pre-structural to extended abstract levels, with cluster analysis identifying key variations among learners. This study underscores the effectiveness of combining TDS and SECI in fostering epistemic knowledge in statistics education. The research highlights the importance of strategies that bridge tacit and explicit knowledge, offering valuable insights for curriculum development and teaching practice improvement in mathematics education.