Digital-age PAUD management innovations: Meeting children's needs through technology
Bibliographic record
Abstract
Management innovations in Early childhood education programs or the so called PAUD (Pendidikan Anak Usia Dini) management based on children's needs in the digital era have become very relevant for improving the quality of early childhood education. Technological developments provide opportunities for educational institutions to adapt learning methods that are more interactive and appropriate to children's developmental needs. This research explored the implementation of technology-based management innovations at PAUD Plamboyan 3, West Karawang, and evaluated their impact on children's learning and parental involvement. The study aims to determine the implementation of technology-based management innovations and to identify their effects on the learning process, parental involvement, and curriculum management. This research used a qualitative approach with a case study method. This qualitative research employs a case study method, with data collected through observations. The sample in this interview consisted of one school principal, five teachers, ten parents, and documents related to technology integration in learning. The research results show that using technology, such as learning applications and e-learning platforms, increases children's learning motivation and strengthens parental involvement in children's education. Curriculum management tailored to children's needs also supports children's cognitive, social, and motoric development. However, some parents need attention to challenges related to limited access to technology at home. This research is essential for the development of PAUD in the digital era by emphasizing the importance of teacher training, increasing access to technology, and the active role of parents in supporting children's learning process.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".