The Relationship between Principals’ Technology Leadership and Teachers’ Technology Use in Malaysian Secondary Schools
Bibliographic record
Abstract
The aim of the study is to examine of technology usage in Malaysian secondary schools and the influence of principals on technology use. This study focuses on principals’ technology leadership behavior according to the National Educational Technology Standards for Administrators (NETS-A). The sample for this study consisted of 115 principals from public schools in Kedah, Malaysia. Two survey instruments were used in this study. First, the Principals Technology Leadership Assessment PTLA survey is to measure the independent variable, Principals’ Leadership Behaviour. Secondly, a TTU (Teachers Technology Use) to measure teachers’ technology use in schools. The relationship between PTLA and TTU was measured using a simple linear regression analysis. The study revealed that the PTLA was not found to be a good predictor of school technology use, F(1, 83) = 12.48, p < .0005 and principals’ technology behavior accounted for 12.1% of explained variability in teachers’ technology use in the classroom. The regression equation is as follows: Teachers’ Technology Use (TTU) = -0.825 + 0.037 (PTLA score). Thus the equation shows that one unit of change in PTLA score could increase the teachers’ technology use by .04. Finally, the implications for principals as well as teachers are 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".