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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".