Peer Observation: A Professional Learning Tool for English Language Teachers in an EFL Institute
Bibliographic record
Abstract
The key aim of this study is to explore the perceptions of English as foreign language (EFL) teachers about peerobservation as a tool for professional development that is implemented in an English Language Institute of a SaudiArabian university. This paper reviews literature on peer observation to develop a conceptual and theoreticalunderstanding of peer observation systems in different contexts. It utilizes a mix-method approach and applies aquestionnaire and semi-structured interviews as data collection tools. Questionnaire is used to get information aboutEFL teachers’ perceptions whereas semi-structured interviews provide an insight into their practices in the form ofpeer observation and future amendments for PD. The participants share their lived experiences who consider thecurrent practice of peer observation a consistent professional challenge due to several factors, i.e. their lack ofautonomy in deciding about the peers, trust deficit between administration and EFL teachers, rarely heldpre-observation conferences due to the loads of teaching hours, observers’ insufficient training and qualifications inconducting PO, and the element of threat and insecurity. Based on the findings, recommendations are made toimprove the existing peer observation system for the benefit of the EFL teachers, English language learners and theinstitute.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".