Thinking outside the box: a systematic review of gamification trends in children’s dance education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it