Analysis of reflection aspects of digital literacy educational content in foreign native language curriculum
Bibliographic record
Abstract
Purpose: By analyzing previous research on digital literacy and the national language curriculum of major foreign countries, we aimed to critically review Korea's media curriculum and suggest ways to improve it. Methods: We analyzed achievement standards related to digital literacy in the national language curriculum of the United States, Canada, and Australia. Implications for media curriculum development were derived by comparing the reflection pattern of digital literacy-related content in each country with the media area of Korea's curriculum. Results: In the U.S. language curriculum, content related to digital literacy is centered on multimodal literacy and information literacy. In the language curriculum of Ontario, Canada, relatively much of the content related to digital literacy is related to participatory literacy and critical literacy. The Australian language curriculum is characterized by reflecting the digital literacy component in the knowledge category and including digital literacy-related content in the literature area. Conclusion: First, the implications for Korea's Korean language curriculum development include the need to reconsider the issue of setting the media area. Second, there is a need to deal more comprehensively with the functional elements of digital literacy. Third, there is a need to systematize the content elements of attitude categories. Fourth, there is a need to systematize the content elements of attitude categories.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 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".