The Development of an Internal Supervision Model Using the Professional Learning Community for Educational Supervisor of Graduate Diploma in Teaching Profession Program
Bibliographic record
Abstract
The objectives of this research were to: 1) study the current and desirable situations of supervision for graduate diploma in teaching profession program, 2 develop the internal supervision model using professional learning community for educational supervisor of graduate diploma in teaching profession program, and 3) evaluate the efficiency of the developed model.The samples were 49 educational supervisors and committees of graduate diploma in teaching profession program. The instrument was handbook of internal supervision model developed by using the professional learning community. Statistics used were percentage, mean, standard deviation, modified priority needs index, efficiency index, and t-test for dependent samples. The finding srevealed that: 1) The current situation of internal supervision of graduate diploma in teaching profession program was in “High” level. In addition, the desirable situation was found in the“Highest” level. When considering each aspect, it was found that the aspect with highest emergency need for development was the planning with the PNImodified of 0.40. The second one was the implementing with the PNImodified of 0.36. 2) The developed model was evaluated in the “Highest” level in terms of the propriety, feasibility, and utility.It was integrated in 6 steps including: (1) Preparing, (2) Planning, (3) Implementing, (4) Reflecting, Improvement, and Evaluation, (5) Reinforcing, and (6) Concluding and Reporting. 3) The results of the trials were: (3.1) The efficiency of process and product (E1 / E2) were84.67/ 83.00 which was higher than the specified criterion 80/80. (3.2) The posttest score was significantly higher than the pretest one at level of p-value <0.01. (3.3) The satisfaction on the developed model was in the “Highest” level. When considering each aspect, it was found that planning had the highest mean score, while implementing was found in the second order, and reflecting, improvement, and evaluation had the lowest mean scores.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".