At the facet level, visualizing data is the most prevalent facet and co-occurs with applying statistics and synthesizing results¶
Claim · All claims
Evidence · 1 study (2 entries) · reviewr2·q3
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person · recheck by 2031: baselines and conditions change
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q3 i? Visualizing data was the most prevalent facet (n = 33), frequently co-occurring with applying statistics (lift = 1.05, support = 0.57) and synthesizing results (lift = 1.07, support = 0.62). → Jina Kang 2026
q3 i? Stronger associations included designing conditions → considering parameters (lift = 2.42, support = 0.14) and designing conditions → designing protocols (lift = 2.30, support = 0.17), with the reverse rule not meeting criteria. → Jina Kang 2026 (2)
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 using the Arules package in R on the 42 studies, with rules retained when lift > 1, support > 0.10, and confidence > 0.50. The review reports "visualizing data emerged as the most prevalent (n = 33)" and its co-occurrence metrics with applying statistics and synthesizing results.
"Among the facets, visualizing data emerged as the most prevalent (n = 33). As shown in Fig. 2, it frequently co-occurred with applying statistics (#13; lift = 1.05, support = 0.57) and synthesizing results (#17; lift = 1.07, support = 0.62), suggesting that visualization was often paired with analytic and interpretive practices in the reviewed studies."
Jina Kang 2026 (2)¶
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-rule analysis of facet co-occurrences across the 42 studies. The review reports the strongest directional rules within Designing Experiments and notes the reverse rule (considering parameters → designing conditions) did not meet the specified criteria.
"Within the Designing Experiments (DE) dimension, the strongest directional associations observed across the reviewed studies appeared between designing conditions → considering parameters (lift = 2.42, support = 0.14) and designing conditions → designing protocols (lift = 2.30, support = 0.17)."
Discussion¶
Related Claims¶
- Collecting and analyzing data are the most prevalent data practices in K-16 science education research, while cleaning and disseminating data are least reported — related
- K-12 and higher education show distinct, non-overlapping sets of co-occurring data practices — related
- Data type shapes practice emphasis: first-hand data aligns with problem understanding and experiment design, second-hand data with statistical analysis and synthesis — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
visualizing-data-most-prevalent-facet-associations |
title |
At the facet level, visualizing data is the most prevalent facet and co-occurs with applying statistics and synthesizing results |
description |
At the facet level, visualizing data is the most prevalent facet and co-occurs with applying statistics and synthesizing results |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2026 |
recheck_by |
2031 |
generated |
by process:wiki-ingest on 2026-10-18 |
trust |
unverified — no one has confirmed this page's content |
sources |
2 — jina-kang-2026, jina-kang-2026-2 |
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