Pre-Service English Language Teachers’ Perceptions of A Good Language Teacher at the University of Technology and Applied Sciences (UTAS), AL-Rustaq, Oman
Bibliographic record
Abstract
The study examines how the pre-service English language teachers at the University of Technology and Applied Sciences (UTAS)-AL-rustaq perceive the notion of a ‘good language teacher’ before and after they undertake microteaching practices. Qualitative research was employed to answer the main research questions of the study, which is what are pre-service teachers’ perceptions of a good language teacher before and after experiencing microteaching? In order to answer the research question, semi-structured interviews for twelve pre-service English language teachers were conducted before and after the microteaching context. By employing the content analysis, the findings revealed that pedagogical knowledge is the best quality that pre-service English language teachers must possess at the UTAS-AL-rustaq. Personal qualities such as being a motivator, being kind, being enthusiastic and having a sense of humor were also perceived as important features of a good teacher from the participants’ point of view. The powerful influence of the microteaching practice made the participants able to incorporate their background knowledge (the studied theories) into practice. This was clear from their interpretation of the concept of good language teacher from only personal qualities to more into teaching with good personal qualities. This, therefore, emphasizes the importance of this practical course in shaping more practical teaching identities over simply theoretical ones amongst the participants. It is hence recommended to increase the practical courses and allocate them towards the end of the pre-service teacher education programme. It is also recommended that the teachers of these courses focus on post-lesson discussions, and make a balance emphasis on the overall good teacher qualities in order for the pre-service English language teachers to see the importance of all qualities in the language classroom.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 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) | 0.004 | 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".