A Model of Causal Relationships Affecting the Effectiveness of Primary Schools under Khon Kaen Primary Education Service Area
Bibliographic record
Abstract
The objectives of this research were 1) to examine the causal relationship model of factors affecting the effectiveness of primary schools which was developed through empirical data, 2) to study the factors that have direct, indirect and overall influence on the effectiveness of primary schools in Khon Kaen. The samples were 640 school administrators including teachers in Khon Kaen primary schools. The data were analyzed by program computer using the method of structural equation modeling (SEM). The research results indicated that the causal relationship model factors affecting the effectiveness of Khon Kaen primary schools consist of 5 latent variables or factors namely: 1) school vision, 2) academic leadership, 3) promotion of school climate and environment, 4) quality teaching, and 5) learned behavior of students. As for causal relationship model of factors affecting the effectiveness of the above-stated schools, it is found that the construct validity is in congruence with the empirical data. The indices of congruence are Chi-Square at 94.793, df = 87, P-value=0.2662, TLI= 0.998, CFI=0.999, SRMR=0.031, RMSEA=0.013, with statistical significance at .01. The factors that directly and positively affect were of statistical significance at .01, are 1) shared vision, 2) academic leadership, 3) teaching quality, respectively, The most indirect influential factors affecting the school effectiveness are 1) shared vision, having indirect influence in positive manner to climate and environment of the schools, affecting in positive manner to teachers’ teaching quality, and 3) academic leadership having indirect influence in positive manner on school climate and environment that affect students’ learning, respectively.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".