Understanding science data literacy: a conceptual framework and assessment tool for college students majoring in STEM

• Main Authors: Cuilan Qiao (Author), Yuqing Chen (Author), Qing Guo (Author), Yunwei Yu (Author)
• Format: Book
• Published: SpringerOpen, 2024-05-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 10.1186/s40594-024-00484-5; 2196-7822
• Keywords: Science data literacy; Conceptual framework; Assessment tools; Delphi; Rasch; Education; L; Education (General); L7-991; Special aspects of education; LC8-6691; Theory and practice of education; LB5-3640
• Source: International Journal of STEM Education, Vol 11, Iss 1, Pp 1-21 (2024)

Abstract Background In the era defined by the fourth paradigm of science research, the burgeoning volume of science data poses a formidable challenge. The established data-related requisites within science literacy now fall short of addressing the evolving needs of researchers and STEM students. Consequently, the emergence of science data literacy becomes imperative. However, notwithstanding the escalating importance of science data literacy, a comprehensive definition and conceptual framework are still elusive, posing challenges to effective assessment. Results This study analyzes the science literacy frameworks of six international organizations and countries, including the OECD, and reviews 16 data literacy-related literature sources identified using the PRISMA process. We also consider the characteristics of science data. Based on these sources, we clarify the connotation of science data literacy and construct a tailored conceptual framework for college students majoring in STEM. The framework undergoes two rounds of Delphi method refinement to enhance its applicability. Subsequently, we created and piloted a set of questions using physics, astronomy, geography, and other STEM subjects as examples to assess science data literacy. The revised assessment tool was then used in a formal test with 198 university students, employing Rasch modeling to evaluate its effectiveness. The tool's validity in assessing science data literacy was confirmed. Conclusions This study offers a systematic and comprehensive conceptual framework for science data literacy tailored to STEM undergraduates. Endorsed by experts, the framework outlines essential literacies for STEM students in handling science data. The developed assessment tool enables educators to measure students' science data literacy levels and serves as a scientific guide to enhance their competencies in this area.