Improving digital literacy in a digital world: Development of an evidence-based digital literacy program and assessment tool to evaluate youth digital literacy
Bibliographic record
Abstract
The rapid expansion of digital connectivity has provided youth with near universal access to digital platforms for communication, entertainment, and education. This unprecedented access to digital devices continues to raise concerns about online safety, data privacy, and cybersecurity. The critical factor influencing the ability of youth to navigate digital platforms responsibly is digital literacy. While some regions across the world have implemented digital literacy programs, inequities and disparities remain in not only overall digital literacy levels, but also evaluation of digital literacy. To address these challenges, an environmental scan was conducted to identify existing digital literacy programs in Canada developed specifically for youth, as well as digital literacy assessment tools. The literature search encompassed peer-reviewed articles, organizational curricula, and assessment measures indexed in various databases. Data was synthesized from identified programs and assessment tools to inform the development of a new digital literacy program, and an assessment tool tailored for youth. The environmental scan identified 15 digital literacy programs targeting various components such as data safety, cyberbullying, and digital media. Based on the findings, a new program was developed focusing on four key components: 1) digital fluency, 2) digital privacy and safety, 3) ethics and empathy, and 4) consumer awareness. Additionally, 12 assessment tools were identified for digital literacy focusing on evaluating several aspects, including searching and processing digital information and digital safety, which informed the development of an assessment tool to complement the new program.Tailored digital literacy programs and assessments are crucial for understanding and addressing digital literacy among youth globally. This program's adaptability allows for customization to various target audiences, including culturally diverse and geographically remote communities, to enhance digital literacy across settings. Implementing digital literacy programs can better prepare youth for an increasingly digital world, while minimizing potential risks associated with technology use.
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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 | 0.053 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".