Total Quality Management Practices and its Implication to Teachers’ Performance and School Success
Bibliographic record
Abstract
the total quality management practices of school play an essential role in achieving school success in meeting its vision and mission. Hence, this study determined total quality management practices and its implication to teachers’ performance and school success. It utilized quantitative and descriptive- correlational approaches to gather data. The respondents of this study were 156 public elementary teachers of tiaong i, district. It was conducted during the third quarter of the school year 2022-2023. It employed the used researcher-made questionnaire validated by educational experts in the research in data gathering procedures. The study revealed that there is a significant relationship between total quality management practices and school success. It is proposed that the school leaders should provide an avenue on providing teachers opportunities for their career growth and development as well as continuously involving them in the school relevant matters in decision-making resulting to boost their leadership and self-esteem. Then, continuous up skilling of instructional skills in terms of curriculum planning and assessing and reporting should be given priority to induce positive learning outcomes. Likewise, institutionalizing of total quality management practices is recommended in school to set clear standards of providing a relevant and quality educational services to the learners. Lastly, for the future researcher, a follow up study can be conducted using a regression analysis to identify other predictors and moderators about total quality management practices that promotes school success.
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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 | 0.002 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".