Pre-Service English Teachers' Perceptions, Obstacles and Experiences When Teaching English in the EFL Context
Bibliographic record
Abstract
This article has identified the opinions, perceptions obstacles and experiences of the EFL pre-service English teacher who participated in a training program in EFL context. In this study, only qualitative data was gathered. The participants in this study were 7 pre-service English teachers aged 23-28 years, from seven boys’ schools in Al-Baha city in Saudi Arabia. The researcher interviewed them at the end of the semester of the training program. The findings of this study were that some EFL pre-service English teachers felt that the training program enabled them to increase their confidence and social skills, enabling them to gain more experiences.However, there were many disadvantages, barriers and obstacles to practicing pre-service teaching in the training program. These included: some EFL students were naughty and they caused problems in the classroom, the EFL students’ English level was very weak such that they could not communicate in the language and even they also could not understand the teacher’s instructions. Being in the training program, and studying at the college at the same time was very challenging for the pre-service English teachers. Also, some their main teachers were not willing to guide them. They were also often shy and embarrassed before the students. There were claims that the preparation book was difficult for most pre-service teachers to prepare. Most complained that they lacked the resources they required to prepare for their lessons.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".