Relationship Between School Leadership, Academic Dispositions, and Student Academic Performance: Meaning Making of PISA 2022 Results
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
School leadership plays a critical role in shaping student academic performance. Despite the UAE’s recognition as one of the leading nations globally for quality education, research on the impact of leadership practices on performance in international assessments like PISA remains scarce. This study explores the influence of school leadership on students’ performance in the UAE’s schools. The PISA 2022 UAE database containing data on 24,600 15-year-old students across 840 schools was used to assess mathematical literacy based on their ability to apply math concepts and their attitudes toward the subject. Insights into leadership practices were utilized using responses from school principals in the PISA 2022 school leaders’ questionnaire. The results demonstrate that leadership practices significantly influence student outcomes. Schools where leaders emphasize teacher accountability and professional development show improved mathematics performance, lower anxiety levels, and enhanced self-efficacy among students. Conversely, excessive focus on disciplinary measures or teaching skill improvements is associated with reduced student self-efficacy. These findings highlight the importance of adaptive leadership approaches that consider local educational contexts, balancing accountability and support to optimize both student performance and well-being. By refining leadership practices, schools can drive meaningful improvements in student success and better equip learners to thrive in global educational benchmarks.
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.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it