Design, Development, and Evaluation of a Cybersecurity, Privacy, and Digital Literacy Game for Tweens
Bibliographic record
Abstract
Tweens are avid users of digital media, which exposes them to various online threats. Teachers are primarily expected to teach children safe online behaviours, despite not necessarily having the required training or classroom tools to support this education. Using the theory of procedural rhetoric and established game design principles, we designed a classroom-based cybersecurity, privacy, and digital literacy game for tweens that has since been deployed to over 300 Canadian elementary schools. The game, A Day in the Life of the JOs , teaches children about 25 cybersecurity, privacy, and digital literacy topics and allows them to practice what they have learned in a simulated environment. We employed a user-centered design process to create the game, iteratively testing its design and effectiveness with children and teachers through five user studies (with a total of 63 child participants and 21 teachers). Our summative evaluation with children showed that the game improved their cybersecurity, privacy, and digital literacy knowledge and behavioural intent and was positively received by them. Our summative evaluation with teachers also showed positive results. Teachers liked that the game represented the authentic experiences of children on digital media and that it aligned with their curriculum requirements; they were interested in using it in their classrooms. In this article, we discuss our process and experience of designing a production quality game for children and provide evidence of its effectiveness with both children and teachers.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".