International Comparative Study on Digital Media Literacy Elements in National Early Childhood Curricula in Finland, Canada, Australia, and South Korea
Bibliographic record
Abstract
Objectives: Digital media literacy has been recognized internationally as an important competence. The 2022 Revised Curriculum for elementary and secondary education reflects digital media literacy as an essential competency for democratic citizenship. While the global trend suggests early childhood as the time for commencing digital media literacy education, the related elements are not specifically contained in the Korean national early childhood curriculum (Nuri Curriculum).</br>The purpose of the current study is to propose a direction for revision of the Nuri Curriculum by comparatively analyzing the educational expectations, structures, and contents related to digital media literacy in early childhood curricula in Finland, Canada(Ontario), and Australia.Methods: Finland, Canada, and Australia implement lifetime media literacy education and systematically include media literacy in national level early childhood curricula. Educational expectations, structures, and contents related to digital and media literacy were analyzed according to media literacy and related skills and elements.Results: First, the educational expectations from international early childhood education curricula reflects digital and media literacy competency. Second, the international curricula suggest active online safety in ways such as participating in a safe, media-friendly environment. Finally, the international curricula encourage active development of digital literacy by suggesting diverse ways of using media.Conclusion: For digital media literacy development in early childhood, the elements of digital media literacy should be more specifically contained in the national level curriculum. Considering the change in the 2022 Revised Curriculum and global trend, it is necessary to reflect competencies in digital media literacy comprehensively in the Nuri Curriculum.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".