Exploring English Language Supervisors’ Competencies from Kuwaiti Student Teachers’ Perspectives: A Study in the College of Basic Education (CBE)
Bibliographic record
Abstract
Supervision is an integral part of the language teacher education process which plays a significant role in improving teachers’ teaching practice. This study explores Kuwaiti English as a foreign language (EFL) female student teachers’ perspective on the performance of their supervisors during the practicum course. It sought to explore the general perspectives associated with supervisors’ supervision competencies with the view to informing improvements to their instructional supervisory skills. 50 EFL female student teachers from CBE participated in the study. Using a case study approach, a combination of a questionnaire and semi-structured interviews was used to generate answers to the research questions. The questionnaire findings suggested that student teachers generally have positive attitudes towards their supervisors’ competencies; however, interviews with participants have shed light on various issues that need to be considered. For example, supervisors’ feedback often focuses on participants’ Content Knowledge (CK) rarely addressing their Pedagogical Content knowledge (PCK). In addition, student-teachers stated that their relationships with their supervisors tended to be formal and authoritative, which prevented them from asking questions freely. This article discusses these findings in detail and highlights some practical implications that are likely to help EFL supervisors to enhance their supervisory practices.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".