Gamified Playgrounds: How Game Dynamics, Mechanics and Strategies Could Help Elementary School Children Learn About Equity and Improve Their Physical and Mental Health
Bibliographic record
Abstract
This paper aims to investigate different concepts, theories, perspectives, and methods related to playing, games, gamification, serious games for learning, and the influence of technology on children's development and well-being. Furthermore, the study explores how physical, immersive digital tools, and experiences can improve primary school children's learning, social skills, and health. Even though the reviewed literature revealed that the study of these fields has immense depth and debate, the investigation in some areas has just started, or the theories could evolve. For this particularresearch, the document will highlight some relevant conceptsand angles to createan adequate theoretical basis to inform the development of a creative artifact—a physical playground aided with technology (NFCs) and gamification to contribute to the well-being of elementary school children. The creation of the prototype could give valuable insights and initiate further research on the areas related to this MRP. Some inconclusive studies and unsubstantiated perspectives present an opportunity to redefine the role of technology in children's development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".