Competency Level of Operators for Private Early Childhood Care and Education in Malaysia
Bibliographic record
Abstract
This study aims to determine the level of competency of operators for private early childhood care and education (ECCE) across Malaysia, towards the development of carer-educator professional framework. The levels of competency included in this study are disposition, knowledge, skills and practices. In this study, quantitative research was conducted and 551 operators were randomly selected from various private ECCE across Malaysia. The ECCE includes children nursery (TASKA) and children preschool (TADIKA). Descriptive and inferential statistics based on one single t-test were applied to the survey data collected in order to answer the research objectives. In general, the study shows that all private ECCE operators have high level of competency in terms of disposition, knowledge, skills and practices except for operators of TASKA and operators that manage both TASKA and TADIKA. This study is important as early childhood education such as TASKA and TADIKA are the places where the foundation of a child’s development and the first formal education for children begins. Future research will investigate the relationship between the level of competency and years of service and Early Childhood Care Education (ECCE) professional qualification of the operators.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".