An Analysis of Digital Media Literacy Education Structures and Teaching Materials in British Columbia, Canada : Focusing on Information and News Credibility Verification and Media Representation
Bibliographic record
Abstract
This study analyzed digital media literacy education in British Columbia’s (BC) English Language Arts curriculum and public schools to inform the effective implementation of digital media literacy education in South Korea, following the introduction of the ‘Media’ domain in South Korea’s revised 2022 Language Arts curriculum. Document analysis was conducted on BC’s English curriculum, the Digital Literacy Framework, and teaching resources from specialized organizations such as CIVIX and Common Sense Media. Findings reveal that BC clearly defines digital literacy concepts and integrates these across multiple subjects, emphasizing language and texts as crucial resources for identity formation, social relationships, and civic engagement. Classroom activities involved verifying online information credibility, differentiating reliable news from misinformation, and creating forced perspective images. Recommendations for Korean education include clarifying core media literacy concepts, expanding integrated instruction, providing opportunities for creative production and critical reflection, and strengthening teacher professional development. Ultimately, digital media literacy education must prioritize critical thinking and responsible civic engagement beyond technical skills.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".