The Morale of Supervision: The Impact of Technical Supervision Skills of Teaching and Learning on Teachers’ Self-Efficacy
Bibliographic record
Abstract
The Minister of Education Malaysia specifically issues Circular 3/1987, which notes that it is the responsibility of the principal or director to practice his position as a supervisor in the management of the teaching and learning process in the classroom. This is show that the importance of supervisory processes. However, oversight can be assigned to senior assistants in the case of any obstacle to its execution. The consistency of monitoring is frequently contested because of several issues and vulnerabilities. Recognizing the importance of supervisory processes to enhance teacher professionalism, this study was conducted to examine the influence of teaching and learning supervision and teacher self-efficacy. This research was conducted among 211 teachers who engaged in the teaching and learning supervision process in 13 primary schools chosen by the Jeli District Education Office, Kelantan. The questionnaire was used to gather data and information. The study also indicates that the dimension of professional supervision skills has the greatest effect on the instructor's self-efficiency. In brief, the evaluation of teaching and learning cannot be overlooked, because the findings of the study suggest that these factors have to do with the self-efficacy of the teacher, and cannot be discounted as a factor in the performance of the school.
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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 | 0.004 | 0.016 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".