Conceptions and perspectives of data literacy in secondary education
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
Abstract Data literacy has been suggested as an important competence that individuals need to succeed in a data‐intensive society. However, there is no common understanding as to what data literacy entails and how it could be developed. Instructional emphasis on developing competence of individuals fails to capture learners' relationship to data in everyday life and limits what they can possibly achieve in data‐rich environments. This paper critically reviews conceptualizations of data literacy in the literature with a focus in K‐12 education. The analysis determined four orientations of data literacy: development of competence, inquiry with data, awareness of personal data and civic engagement. I proposed a broader conceptualization of data literacy that integrates conceptions, competencies and contexts. The study offers holistic and context‐oriented framing of data literacy for researchers and educators. Practitioner notes What is known about the topic Data literacy is a potential buzzword in the recent literature. There are increasing calls for developing data literacy skills of students and the general public. Data literacy is framed and implemented as a technical competence. Accordingly, curricular interventions and pedagogical practices focus on making use of data and benefiting from available datasets. What this paper adds The above framing of data literacy is too narrow to be useful in everyday life and rarely considers individuals interaction with data outside of schools. This study develops four focus areas in the conceptualization of data literacy and suggests broader framing of the concept as it relates to everyday life. It also suggests context‐oriented approaches to data literacy education that can go beyond classrooms and academic activities. Implications for practice and policy This paper has implication for educators, researchers and policy makers. It allows boarder conceptualizations of data literacy that can be used in curricular interventions. It also provides ways of designing learning environments for the data literacy education and research.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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