A Theoretical Framework for Interdisciplinary Instructional Design Targeting Statistical Reasoning

Yutong Wang; Qi Shen

doi:10.71204/as5s4879

Authors

Yutong Wang Beijing Union University Author
Qi Shen Beijing Union University Author

DOI:

https://doi.org/10.71204/as5s4879

Keywords:

Interdisciplinary Teaching, Statistical Reasoning(SR), Project-Based Learning (PBL), Primary Education

Abstract

In the digital age, statistical reasoning (SR) is a core literacy for citizens navigating an information society. However, primary school statistics instruction has been striving to address the fragmented development of SR, disconnect between context and real life, and lack of wholeness in statistical activities. To bridge these gaps, this study proposes a novel interdisciplinary Project-Based Learning (PBL) framework grounded in Jones's four-dimensional SR model. Theoretically designed around the theme "Data-Driven Low-Carbon Action," the framework features a dual-path structure: "Longitudinal Competency Progression" (a sequenced progression through the four dimensions of Statistical Reasoning (SR), namely Describing Data Displays (D), Organizing and Reducing Data (O), Representing Data (R), and Analyzing and Interpreting Data (A)); combined with "Horizontal Disciplinary Collaboration" (integrating knowledge and practices from Mathematics, Science, Information Technology, and Chinese Language Arts). Driven by a genuine societal issue, this integrated structure aims to systematically address all dimensions of SR through a "Problem → Task → Product" spiral progression, enabling students to conceptually participate in the complete PPDAC cycle (Problem, Plan, Data, Analysis, Conclusion). This proposed framework offers a structured approach for fostering the synergistic development of students' scientific inquiry, digital innovation, language application, and SR, providing a theoretically grounded and replicable model for interdisciplinary thematic teaching design.

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