Thinking outside the box: a systematic review of gamification trends in children’s dance education
Bibliographic record
Abstract
Since the 21st century, gamification technology has been widely applied across various research domains. In the field of children’s dance, it has exerted significant influences on learning, software development, and therapy. However, a comprehensive review of its application in children’s dance is lacking. This study conducted a systematic literature review selecting 31 relevant articles, aiming to reveal the trends and directions of gamification technology in the domain of children’s dance. The findings indicate: 1) The United States is the most active in children’s dance gaming, followed closely by the United Kingdom and Canada. Academic contributions are primarily published in academic journals in the fields of medicine, sports science, and education. 2) The research mainly revolves around interactive dance games, physical dance games, therapeutic interventions, software development, and teaching methods. 3) Utilizing grounded theory, it explores its core strengths and research limitations. 4) Through in-depth analysis of existing literature, future research focuses and directions are proposed. This aids in promoting the development of more comprehensive, effective, and professional teaching practices and tools.
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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 | 0.013 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".