Bibliographic record
Abstract
Teaching supervision is carried out by principals, and its purpose is to evaluate teachers’ teaching practices in the classroom. This study addresses a gap in the teaching supervision literature, which relates to the fact that studies in the teaching supervision literature have overlooked the obstacles principals face when supervising teachers in Saudi Arabia. The study was conducted over 10 days. Using semi-structured interviews, the data was collected from seven female principals in secondary schools. The results showed that the obstacles faced by principals fall into the following two categories: (a) obstacles to supervision caused by some teachers’ unhappiness about being observed in the classroom; and because some of them fail to admit to having faults; and (b) obstacles that centre on the classroom environment itself, including noisy students, boring classes, a large number of students, small classrooms, the large number of teachers in a single school, and having to supervise all the subjects. These findings indicate that: (a) principals encounter certain obstacles to supervising teachers which affect the quality of the supervision itself; and (b) supervision planners may be motivated to consider the obstacles faced by principals in their supervision of teachers, so having this information can be crucial for improving principals’ performance of supervision in Saudi Arabia.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".