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Record W4404316745 · doi:10.22329/jtl.v18i2.8788

Urban Pre-service English-as-a-Foreign-Language (EFL) Teachers’ Challenges During Teaching Practicum in Rural Schools: A Photovoice Phenomenological Approach

2024· article· en· W4404316745 on OpenAlexvenueno aff
Heri Mudra

Bibliographic record

VenueJournal of Teaching and Learning · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsPhotovoicePracticumPedagogyPhenomenology (philosophy)PsychologyMathematics educationEnglish as a foreign languageEnglish languageSociologyMedical educationMedicineArt

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.012
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.261
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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