Teacher Motivation and Morale Influencing the Effectiveness of Bangkok Metropolitan Administration Schools
Bibliographic record
Abstract
This study aimed to examine the teacher motivation and morale in Bangkok Metropolitan Administration schools, the school effectiveness, the relationship between the factors of teacher motivation and morale in performing their jobs that influence school effectiveness, and the development of guidelines for enhancing teacher motivation and morale in relation to school effectiveness. In the study, the researcher employed a mixed research methodology. In the initial phase, questionnaires were used to collect data. The population consisted of Bangkok Metropolitan Administration school teachers who served their duty in 2022. A multistage random sampling selected 375 persons in total. Mean, percentage, and standard deviation were applied as descriptive statistics. In the last phase, the researcher conducted in-depth interviews with seven experts selected through purposive sampling to gather their perspectives on the applicability, possibility, and usefulness of the the guidelines for enhancing teacher motivation and morale in relation to school effectiveness. The data was analyzed using a content analysis method. According to the findings, the multiple correlation coefficient was .760 (R = 0.760 at the .05 level of significance. The predictive coefficient or predictive power of 57.7 percent (R2 = 0.577), with the regression coefficient () arranged in descending order: 1) Professional Success (β=0.321) 2) Career Growth (β=0.238) 3) School Policies (β=0.162) 4) Workplace atmosphere and environment (β=0.102) 5) Governance Aspects (β=.096). The forecast equations can be generated using the regression coefficients of the predictors in raw score (b) and standard score () as follows: In raw score (unstandardized score) form, the forecast equation is Y' = 1.391 + 0.255 (x 6 )0.183 (x 10 x 10) 0.124 (x 5 (x 5 )0.050 (x 3 (x 3 )0.075 (x 2 (x 2). Standardized score forecast equations Zy = 0.321 (x 6) + 0.238 (x 10) + 0.162 (x 5) + 0.102 (x 3) + 0.096 (x 2). The researcher also devised a guideline containing nineteen recommendations for enhancing the top five teacher motivation and morale factors that influence school effectiveness. There are 19 guidelines for improving teacher morale, which affects school effectiveness.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".