Navigating Professional Identity: Insights into the Effects of Post-Observation Conferences on Educational Supervisors
Bibliographic record
Abstract
This research endeavors to investigate the influence of post-observation conferences (POCs) on the professional identity of educational supervisors within public education settings. POCs represent dialogic sessions conducted between supervisors and teachers subsequent to classroom observations, where they deliberate on the strengths and areas for enhancement in the observed teaching practices. Professional identity, in this context, pertains to the self-understanding and self-image concerning one's role and responsibilities as an educational supervisor. Employing a qualitative, narrative inquiry research design, the study utilizes semi-structured interviews and a focus group as data collection methods. The study involves two English language educational supervisors located in Makkah, Saudi Arabia, as participants. Data analysis is conducted through thematic analysis, guided by Braun and Clarke's (2006) six phases. The findings underscore the significant impact of POCs on the professional identity of educational supervisors, facilitating avenues for learning, feedback, reflection, and collaboration. Moreover, the study identifies challenges and offers suggestions to enhance the quality and effectiveness of POCs. By contributing to the literature on educational supervision and professional development, this study furnishes educational implications for practice.
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 | 0.015 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".