Exploring the Impact of Integrating the Nursery Teacher Certificate on Student Teachers’ Teaching Competences in Teacher Education in Shaanxi, China
Bibliographic record
Abstract
This quantitative study investigates the impact of integrating the Nursery Teacher Certificate into teacher education in Chinese vocational and technical colleges, specifically focusing on the perspectives of student teachers majoring in Early Childhood Care Education. The study aims to assess the influence of integrating the Nursery Teacher Certificate into nursery teacher education on the teaching competences of student teachers. A sample of 95 respondents, enrolled in the Early Childhood Care Education program across five vocational and technical colleges in Shaanxi, China, participated in the study. The findings reveal that integrating the Nursery Teacher Certificate has a positive impact on student teachers’ knowledge, skills, and attitudes, enhancing their practical teaching competence. However, the certificate program neglects the cultivation of classroom management skills in the teacher education, highlighting the need for a more comprehensive approach to address this gap. These findings have implications for the revision of certificate curricula to incorporate explicit training in classroom management and emphasize the importance of nurturing well-rounded teaching competences. By addressing this discrepancy, teacher education programs can better prepare future nursery teacher educators for the challenges they may face in their teaching careers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".