How Principal Strategies in Developing Teacher Professional Competence Improve Educational Quality: A Systematic Review
Bibliographic record
Abstract
Improving the quality of education hinges significantly on the leadership strategies of school principals, especially in the systematic development of teacher professional competence. This systematic review explores the strategic approaches employed by school principals to foster teacher professional competence and, consequently, improve educational quality. Employing a qualitative Systematic Literature Review (SLR) guided by PRISMA standards, this study synthesizes research published between 2020–2025, emphasizing frameworks such as TCCM and best practices in educational leadership. The findings reveal principal strategies that integrate instructional leadership, collaborative professional development, data-driven decision-making, inclusive school culture, and adaptive change management. Challenges persist, including resource constraints, varying teacher motivation, and policy-practice misalignments, but successful strategies hinge on collaborative environments, targeted professional development, continuous evaluation, and fostering a culture of innovation and resilience. The review provides robust theoretical and practical recommendations for policymakers, educational leaders, and practitioner communities seeking to elevate educational outcomes via strategic principal leadership in teacher competency development.
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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 | 0.047 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".