A Comparative Study in the Effectiveness of Interactive E-books to Teach Children Online Privacy and Security
Bibliographic record
Abstract
The purpose of this study was to evaluate whether interactive e-books could as effectively teach online privacy and security to children ages 7 through 10 in Maryland, Virginia, and Maine as to Canadian children. The research replicated a study performed in Canada (Zhang-Kennedy & Chiasson, 2016), to see if the effects are the same. The study also investigated the persistence of the privacy models held by Canadian children identified prior to the COVID-19 pandemic, which had informed the design of the interactive e-book, amongst children in the Eastern Coast region of the United States. Fifteen parent and children pairs completed the study, which included a device criteria questionnaire, usability evaluations, a pre-privacy knowledge assessment before co-reading session, and a post-privacy knowledge assessment after a ten-minute distractor. Data analysis was conducted for all 15 parent-child pairs. During initial synthesis it became evident that the design of the interactive e-book was not suitable for children aged 10, which confirmed the intention of the original Canadian researcher to target young children aged 7 through 9. Therefore, results for two child participants aged 10 were excluded for the analysis that evaluated the e-book’s effectiveness, but their results were included in the analysis for the persistence of privacy models. Children in the study showed an increase in comprehension of online security and improvement on safety conscious behavior similar to the study involving Canadian children. However, children in the United States had less positive experiences with the interactive e-book than children in the Canadian study. Three mental models of privacy were found to have persisted amongst the child participants in the United States: ‘to be alone’, ‘to hide secrets/special things’, ‘to keep things to yourself’. One model did not persist after reading the e-book: ‘to not talk to strangers’ but evolved into ‘don’t trust strangers’. One new model was identified, ‘don’t let anyone see you’. Additionally, the study identified some of the ways that children’s mental models of the world were impacted by the COVID-19 pandemic. Ultimately, the goal is to provide further empirical evidence and insight to inform the design of better cybersecurity tools for young children.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".