Instructional Leadership Practices Among Headmasters and The Correlation with Primary Schools’ Achievement in Sabah, Malaysia
Bibliographic record
Abstract
This study aims to identify the correlation between the instructional leadership practices by the headmasters with the level of performance and achievement of schools in Malaysia. A total of 141 respondents comprised of senior assistants of primary schools were examined. Respondents' perceptions of instructional leadership practices by the headmasters were gained using a set of questionnaire that was modified from the Principal Instructional Management Rating Scale (PIMRS). Descriptive statistical analysis was used to obtain the mean score, percentage, and standard deviation of the instructional practices. At the same time, ANOVA test was applied to obtain the perceptions from demographic factors and Pearson Correlation to measure the relationship between the instructional leadership and the schools’ performance that is based on band measurement. The findings show that the level of instructional leadership practice is high, with a mean of 4.24. Band 1, 2, 3 and 4 schools each have min of 4.37 4.23, 4.23 and 4.05 respectively. Pearson correlation analysis shows that there is a weak negative correlation between the headmasters’ instructional leadership practices level and the performance of the schools (r = -0.210). Thus, findings conclude that the level of instructional leadership practices among headmasters in primary schools is high and has a weak negative relationship in-term of schools’ achievement.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".