The Design, Development and Evaluation of a Digital Literacy Game for Preteens
Bibliographic record
Abstract
The goal of our research is to develop an effective and empirically validated persuasive digital literacy game for tweens, meant for classroom use.Our secondary goal is to explore whether procedural rhetoric is an effective approach for designing digital literacy games.We developed the game in collaboration with MediaSmarts, a not-for-profit organization.The game, A Day in the Life of the JOs, is available in both English and French, and accessible on desktop computers and tablets.Thus far, the game has been launched in over 300 Canadian elementary schools.We conducted two user studies to evaluate the usability and effectiveness of the game with children and teachers.We found that the game was usable, effective at improving children's digital literacy, and was positively received.Our study with teachers show similar trends, and indicate that the game meets curriculum requirements, and can be used in classrooms.We also provide a model to conceptualize issues that teachers identify with children's use of digital media, and how they help them resolve these issues.Many individuals were involved in supporting me throughout this journey.First, I would like to thank my amazing supervisor Sonia Chiasson for her time, guidance, and compassion during my studies at Carleton.She took me on as an eager undergraduate student, and her dedication and expertise has shaped me into the researcher and person that I am today
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".