Identifying the Social Development Needs of Preschool Children: A Teacher-Informed Needs Analysis
Bibliographic record
Abstract
Social development during early childhood is critical for building the foundations of emotional regulation, peer interaction, and school readiness. This study examined preschool children’s social development needs through a teacher-informed needs analysis conducted with 50 preschool teachers. Data were collected using the Preschool Children’s Needs for Social Development Questionnaire, a 25-item instrument measuring five domains of social competence: Social Emotion and Behavior, Conflict Resolution, Language Expression, Cooperation and Sharing, and Play Together. Descriptive statistical results revealed moderate levels across all five domains (M ? 3.00 on a 5-point scale), indicating that while children are willing to participate in social interactions, their prosocial skills are inconsistent. The lowest mean score was found in Cooperation and Sharing (M = 2.99, SD = .99), followed by difficulties in empathy-related behavior and verbal conflict resolution. These findings suggest that preschoolers require structured, guided opportunities to practice prosocial behavior rather than relying solely on free play. Implications highlight the need for intentional social-emotional learning and guided play experiences, and the study recommends implementing structured play-based interventions such as the GATHER Play Module to enhance children’s cooperative and empathetic behaviors in natural classroom settings.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".