Navigating the digital world: development of an evidence-based digital literacy program and assessment tool for youth
Bibliographic record
Abstract
Abstract The rapid expansion of digital connectivity has provided youth with wide-ranging access to digital platforms for communication, entertainment, and education. In light of this profound shift, there have been growing concerns about online safety, data privacy, and cybersecurity. A critical factor influencing the ability of youth to responsibly navigate digital platforms is digital literacy. While digital literacy programs have been implemented in various regions worldwide, significant disparities remain not only in overall digital literacy levels, but also the assessment of digital literacy initiatives. To address these challenges, an environmental scan and literature review were conducted to identify existing digital literacy programs in Canada developed specifically for youth, as well as digital literacy assessment tools, respectively. The search encompassed peer-reviewed articles, organizational curricula, and assessment measures indexed in various databases and organization websites. The environmental scan identified 15 programs targeting key components of digital literacy such as data safety, cyberbullying, and digital media. The literature review identified 12 digital literacy assessment tools. Based on the findings, data were synthesized from shortlisted programs and assessment tools to inform the development of both a new digital literacy program and assessment tool to complement the youth-focused program. The new program focuses on four key components: (1) digital fluency, (2) digital privacy and safety, (3) ethics and empathy, and (4) consumer awareness. A 15-item assessment tool was also developed consisting of 4–5 questions specific to each program component. Given the growing importance of digital competencies, a youth-focused program and assessment tool are crucial for understanding and addressing digital literacy among this vulnerable cohort. This program's adaptability allows for customization across sociodemographic target groups, including culturally diverse and geographically remote communities—an aspect that has the potential 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 | 0.062 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".