A Model for Supervision Management to Improve the Education by Using the Area as Base in Digital Era under Primary Educational Service Area in the Northeastern Region
Bibliographic record
Abstract
Educational reform in the modern era which the school administrators and all personnel in the school Must improve and develop themselves to keep up with changes in the era of globalization. By using the supervisory management model process to develop educational quality, these research objectives are to: 1) Study the problems and elements of supervision. Target group: 15 people 2) Create a supervisory model. By conducting in-depth interviews with 15 people involved. 3) Experiment with the supervision model with a sample group of 3 schools 4) Evaluate the use of the supervision model. By organizing a seminar with 15 experts and asking for opinions about its usefulness. Feasibility, appropriateness, and correctness of the format from 291 study supervisors. The instrument used was a questionnaire. Statistics were used to find the mean and standard deviation. It can be summarized as follows: 1) Problems in supervision include an insufficient number of supervisors. Study supervisors lack knowledge Lack of good supervision skills and no systematic planning. There are 5 important elements as follows: (1) objectives, (2) planning, (3) supervision, (4) monitoring and reflection, and (5) development and application. 2) Creating a model for supervision, including (1) objectives,(2) content of supervision, (3) process, (4) method, (5) supervisor and supervisor, (6) duration, (7) planning, (8) execution, (9) Evaluation (10) teamwork (11) network building (12) knowledge management (13) learning and quality development (14) building morale and (15) improving development. 3) The results of the trial use of the model had a reliability value of .80. Average comparison results in Knowledge before training and after training, tested with a t-test, and were found to be different. Statistically significant is at the .01 level. They were satisfied with the model. Overall, it is at the highest level (x̅ = 4.90, S.D. = 0.53) and the results of the overall evaluation of the use of the format are at the highest level (x̅ = 4.89) and overall satisfaction with the use of the format is at the highest level (x̅ = 4.63) 4) Model evaluation results are useful feasibility, suitability, and correctness. Overall, it is at the highest level (x̅ = 4.63, S.D. = 0.12).
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".