Teacher Motivation and Morale Influencing the Effectiveness of Bangkok Metropolitan Administration Schools
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it