The Team-Based Internal Supervision System Development for the Primary Schools under the Office of the Basic Education Commission
Bibliographic record
Abstract
Internal supervision in the school is currently experiencing various problems. Supervision preparation problems are related to: lacking of supervision plan, lacking of holistic and systematic planning, and lacking of analysis in current conditions or requirements. While supervision operational problems are included: lacking of supervision cooperation, lacking of knowledgeable and skillful supervisors, and lacking of feedback to supervisees. Problems in evaluation are included: lacking of ongoing supervision and monitoring in a systematically and continually manner. Whereas, supervision is t continuation of a system based on the participation of all parties involved. This is a procedure in management of academic for controlling quality in education.The research aimed 1) to study the components of the team-based internal supervision system for the primary schools under the Office of the Basic Education Commission, 2) to examine the current states and desirable characteristics of the team-based internal supervision system, 3) to develop the team-based internal supervision system, and 4) to evaluate the use of the team-based internal supervision system for the primary schools under this study. The research and development process was divided into four stages: 1) System study and analysis: it was concerned with studying and analyzing the components evaluated by the experts; current states and desirable characteristics were studied from 380 primary schools. 2) System design and development: in this stage data obtained in the first stage were used to design and develop the team-based internal supervision system; three experts evaluated the system. 3) System application: the system was experimentally used in two primary schools which were different in size; the tool used was a handbook on a system. 4) System evaluation: it was a summarization of the results of the system use. Statistics used in data analysis were percentage, mean, standard deviation, and PNI Modified.The research found that the team-based internal supervision system for the primary schools under the Office of the Basic Education Commission was composed of 4 main and 17 sub-components. Input consisted of 6 sub-components, process consisted of 6 sub-components, output and outcome had 3 sub-components, feedback consisted of 2 sub-components. The current conditions of supervision in elementary schools under the Office of Basic Education Commission, had to perform internal supervision at a moderate level. The desirable conditions of the team-based supervision were at most desirable level. There were six sub-systems have been developed. Following the use of the system, it was found that the supervision team gained an increased knowledge and ability in the internal supervision. All of the teachers of the two primary schools in the study acquired a better knowledge on the system. They also were better equipped with an ability to organize the instructional process more effectively than before. They had a better teaching behavior as well.
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 | 0.006 | 0.011 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".