The Impact of Teacher Quality Management on Student Performance in the Education Sector: Literature Review
Bibliographic record
Abstract
High-quality talents come from high-quality education and management, which largely depends on teacher quality. However, varieties of environmental forces are driving change in education, which impacts students' performance greatly. These challenges call for teacher quality management firmly on the agenda of all the school factors. Teacher quality in schools and institutions is one of the most important factors that influence student performance. This review paper aims to classify the connection between teacher quality management and student performance through three dimensions, namely classroom management, teacher qualification, and in-service training. In this literature review, the authors use past studies to certify the quality management and related theories that are used in vocational education. From this study, it reaches three conclusions: firstly, it can be concluded that school leaders can manage teacher quality through the supervision of classroom management, teacher qualification and in-service training. Then, it tries to highlight the significant relationship between teachers' classroom management. Finally, it focuses on enhancing teacher quality according to quality management criteria, it is a practical and effective strategy to cultivate qualified students. This research will help the leaders realize the importance of teacher quality management and strategies that improve teacher quality, thus impacts on student performance.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".