K-12 and higher education show distinct, non-overlapping sets of co-occurring data practices¶

Claim · All claims
Evidence · 1 study · review r2 · q3
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person · recheck by 2031: baselines and conditions change

Subclaims¶

q3 i? Association analyses revealed distinct sets of co-occurring data practices in K–12 and higher education with no overlapping association rules: higher education emphasized second-hand data analysis, while K–12 focused on first-hand data production with hypothesis development, argumentation, and sharing. → Jina Kang 2026

Evidence¶

Jina Kang 2026¶

Jina Kang, Chungsoo Na, Morgan Diederich, Hillary Swanson, Lili Yan. (2026). Cultivating science data literacy in K-16 science education through data practices: A systematic review. Instructional Science. https://doi.org/10.1007/s11251-026-09803-5

q3 · i? · review · r2

Association analysis of data practice facets by grade level (Fig. 6), aggregating studies into K–12 (n = 19) and higher education (n = 21) with two mixed-level studies excluded. The review reports no overlapping association rules between the two categories and characterizes K–12 as emphasizing contextual understanding and hands-on approaches.

"Notably, the results revealed distinct sets of co-occurring data practices in K–12 and higher education, with no overlapping association rules (i.e., no common combinations of practices) observed across the two categories. Higher education predominantly emphasized second-hand data analysis (e.g., using given data, applying statistics or computational methods, and selecting methods), while K–12 focused on first-hand data (i.e., producing their own data ) along with other data practices such as developing hypotheses, identifying variables , gathering information , making data-driven arguments , synthesizing results, and sharing results."

Discussion¶

Related Claims¶

• Data type shapes practice emphasis: first-hand data aligns with problem understanding and experiment design, second-hand data with statistical analysis and synthesis — related
• At the facet level, visualizing data is the most prevalent facet and co-occurs with applying statistics and synthesizing results — related