Identifying popular, rejected and neglected children in Chinese preschool: An exploratory study on the educational application of spatial positioning data
Bibliographic record
Abstract
Amidst the rapid advancement of Internet of Things (IoT) technology and the burgeoning field of Multimodal Learning Analytics (MMLA), this study employs spatial positioning technology as a case study to investigate the potential of multimodal data in assessing children's social development. This study combines the spatial positioning data of preschool children collected during free play sessions in natural educational settings and the spatial metrics constructed based on observational studies to establish and validate a sociometric status Decision Tree classification model. The findings suggest that the model can overall accurately identify children with three distinct sociometric statuses, albeit with some variability in efficacy across different sociometric groups and age groups. Notably, the model demonstrates a high hitting rate in identifying the potentially neglected children, providing valuable support for educators in understanding and fostering children's developmental needs. This study also highlights the advantages of emerging technology and multimodal data application in child development assessment. • Spatial positioning data is applied to assess preschool children's sociometric status. • A Decision Tree classification model was established and verified. • We found that the classification model should take age into account. • Spatial positioning data is proven applicable in child social development assessment. • Findings contribute to application of multimodal data in child develop assessment.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".