Linear and nonlinear relationships between instructional leadership and teacher professional learning through teacher self-efficacy as a mediator: a partial least squares analysis
Bibliographic record
Abstract
Abstract Although the investigation of instructional leadership and teacher professional learning is well-documented in the literature, one overlooked question concerns the linear and nonlinear relationships between these two variables. This study aims to examine the linear and nonlinear relationships of principal instructional leadership on teacher professional learning through teacher self-efficacy as a mediator. This study has collected 335 teacher samples encompassing both primary and secondary school levels in Penang, Malaysia. The analysis of data utilised partial least-squares structural equation modelling. The findings indicated a significant positive linear relationship between instructional leadership and teacher-professional learning. Likewise, there exists a significant mediating effect of teacher self-efficacy between instructional leadership on teacher professional learning. There exists a significant nonlinear relationship between principal instructional leadership on teacher self-efficacy and teacher professional learning respectively. The structural model exhibits a significantly high level of predictive power for in-sample and out-of-sample. This study offers theoretical and methodological advancements in comprehending the complex relationships between instructional leadership and teacher outcomes. It proposes that forthcoming studies could adopt a combination of linear and non-linear relationships to achieve robust empirical findings.
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.
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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".