Data literacy assessments: a systematic literature review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the exponential increase in the volume of data available in the 21st century, data literacy skills have become vitally important in work places and everyday life. This paper provides a systematic review of available data literacy assessments targeted at different audiences and educational levels. The results can help researchers and practitioners better understand the current state of data literacy assessments in terms of issues related to 1) educational levels and audiences; 2) data literacy definitions and competencies; 3) assessment types and item formats; and 4) reliability and validity evidence. The results from the present review led us to conclude that teaching and assessing data literacy is still an emerging field in education. Therefore, high-quality assessment tools are greatly needed to provide valuable insights for students and instructors to monitor progress as well as facilitate and support teaching and learning.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it