Multimedia Approaches for Improving Children's Privacy and Security Knowledge and Persuading Behaviour Change
Bibliographic record
Abstract
By grades 4 to 11, 98% of Canadian children have Internet access outside of school. Computer security and privacy technology reduces children's online risks, but the success of such technology is also dependent on individuals' behaviour that could be improved through education and training. We studied the effects of multimedia educational tools on children's privacy and security knowledge and behaviour. Our qualitative study of children's privacy perceptions showed that they have a poor understanding of privacy and security threats. Using design principles from persuasive technology and instructional design, we designed tools that teach children about privacy and security concepts. We created an online interactive comic and evaluated it with children 11 to 13 years old, and an interactive ebook for children 7 to 9 years old. Both user studies showed superior improvements in children's privacy knowledge, retention, and privacy-conscious behaviour compared to text-only formats. Children found these tools engaging, easy to use, and easy to learn. From these empirical findings, we find that multimedia educational tools create engagement, extend learning, and have the potential to influence children's behaviour in the longer term.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".