School principal’s training programs, challenges, and improvement opportunities: rapid review
Bibliographic record
Abstract
Effective school leaders, with relevant training programs, high-quality management, and in-service pedagogical training, are recognized for their ability to positively influence student performance (Sanfo, 2020). In this study, we focus on analyzing training programs for school principals, assessing aspects such as their strengths, shortcomings, opportunities, as well as potential challenges. The aim was to identify the most effective models for training and preparing school principals in order to optimize their impact on educational success. To this end, we conducted a rapid review of 27 articles from scientific and gray literature. The results of this rapid review will be discussed with a view to an in-depth reflection on the strengths, challenges and opportunities inherent in the various training methods. The analysis shows that school principals’ training is vital in the sense that it prepares trainees for their demanding and increasingly complex future roles. However, these programs sometimes suffer from shortcomings related to the selection process, the consistency between what is taught and what is experienced in the field, and the incoherence of the content of the training curriculum. The analysis also highlighted some opportunities that could improve these programs if integrated, as well as factors that could be barriers to the correct implementation of these valuable training programs.
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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 | 0.026 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".