Urban Pre-service English-as-a-Foreign-Language (EFL) Teachers’ Challenges During Teaching Practicum in Rural Schools: A Photovoice Phenomenological Approach
Bibliographic record
Abstract
While many previous studies focused on English-as-a-Foreign-Language (EFL) teachers’ teaching practices in urban schools, little study has been indulged regarding pre-service English teachers’ (PSTs) challenges and experiences to practice teaching in a rural school. This study aimed to explore various difficulties encountered by urban PSTs during teaching practicum (TP) in rural schools. A total of seven PSTs enrolled in urban universities were voluntarily involved in a study that involves people’s senses and perceptions, rather than scientific evidence, also known as a phenomenological study. Data were collected through multiple semi-structured interviews, followed by a photovoice approach through which the participants were asked to take emotional photos. The results revealed that the PSTs encountered four main challenges during TP in rural schools, including changes in teacher identity construction, intercultural sensitivity barriers, a lack of supporting resources, and limited teacher professional development. Each challenge was accomplished by photographs representing PSTs’ emotions and feelings, such as a collection of dolls, natural scenery, old-printed books, and blank paper. In conclusion, integrating emotional photos into descriptive exploration is paramount evidence of how the challenges were encountered, managed, and solved for better future teaching and learning 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".