The Relationship between Headteachers’ Distributed Leadership Practices and Teachers’ Motivation in National Primary Schools
Bibliographic record
Abstract
The distributed leadership approach have been practiced in most developing countries approximately more than centuries ago as an alternative to school leadership in effort to increase the student outcomes. This article aims to report and discusses findings of a study on identifying the level of distributed leadership practices among headteachers and the level of teachers’ motivation in primary schools in Malaysia and the relationship between these variables. The respondents were 243 teachers from 12 national primary schools in Port Klang, Klang, Selangor. Random sampling technique was used to ensure that each element in the population stood a fair chance to be selected as samplings. This is a quantitative study using questionnaires as research instrument. A descriptive analysis (mean and percentage) was used to identify the level of distributed leadership among headteachers. A Pearson Linear Correlation Test was used to determine the relationships between four dimensions in the independent variable components (distributed leadership practices) and dependent variable (motivation). The research findings showed that the overall mean score for the level of distributed leadership among headteachers was high (mean = 3.94; SD = 0.484). While the overall mean score for the motivation level of teachers was moderate (mean = 3.11; SD= 0.562). The research findings also showed that there was no significant relationship between headteachers leadership (correlation coefficient value r = 0.279) and teachers motivation. The implications of the research findings on leadership and teachers motivation were further discussed.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".