MétaCan
Menu
Back to cohort
Record W4281746126 · doi:10.1111/bjet.13246

Conceptions and perspectives of data literacy in secondary education

2022· article· en· W4281746126 on OpenAlexafffund
Engida Gebre

Bibliographic record

VenueBritish Journal of Educational Technology · 2022
Typearticle
Languageen
FieldDecision Sciences
TopicEducational Assessment and Improvement
Canadian institutionsSimon Fraser University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsConceptualizationFraming (construction)Competence (human resources)LiteracyEveryday lifeCritical literacyInformation literacyPedagogyPsychologyComputer scienceSocial psychologyPolitical scienceEngineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.004
Science and technology studies0.0060.038
Scholarly communication0.0200.010
Open science0.0010.011
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.080
GPT teacher head0.437
Teacher spread0.357 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations53
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueBritish Journal of Educational TechnologySame topicEducational Assessment and ImprovementFrench-language works237,207