Statistical Analysis of Elements of Movement in Musical Expression in Early Childhood Using 3D Motion Capture and Evaluation of Musical Development Degrees Through Machine Learning
Bibliographic record
Abstract
This study aims to analyze the developmental characteristics of early childhood musical expressions from aviewpoint of movement elements, and to devise a method to evaluate the development regarding musical expressionin early childhood using machine learning. Previous studies regarding motion capture have shown analysis resultssuch as specific actions and responses to music (Burger et al, 2013). In this study, firstly, ANOVA was attempted onfull-body movements. The author quantitatively analyzed the motion capture data regarding 3-year-old, 4-year-old,and 5-year-old children in the nursery schools (n=84) and kindergartens (n=94) through a three-way non-repeatedANOVA. As a result, a statistically significant difference was observed in movement of body parts. Specifically, righthand movement such as moving distance and the moving average acceleration showed a significance of difference.Secondly, machine learning (decision trees, Sequential Minimum Optimization algorithm (SMO), Support VectorMachine (SVM) and neural network (multilayer perceptron)) was deployed to build classification models forevaluation of degree of musical development classified by educators with simultaneously recorded children’s videowith associated motion capture data. Among varieties of trained classification models, multilayer perceptron obtainedbest results of confusion matrix and showed fair classifying precision and usability to support educators to evaluatechildren’s achievement degree of musical development. As a result of the machine learning of multilayeredperceptron, the movement of the pelvis has a strong relationship with musical development degree. Its classificationaccuracy found consistent to affirm the availability to utilize the model to support educators to evaluate children’sattainment of musical expression.
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.
How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".