The Effect of Service Training on English Teachers’ Beliefs and Attitudes
Bibliographic record
Abstract
In-service training (IST), deemed vital for faculty growth, primarily aims to aid instructors in enhancing their teaching skills, with the expectation that their participation will subsequently enhance classroom performance, resulting in improved educational outcomes for students. This study investigates the impact of service training on the beliefs and attitudes of English teachers. To achieve this objective, semi-structured interviews were conducted via Zoom to explore the instructors’ perspectives regarding the perceived service training and its effects on the teaching and learning process. The study delves into the experiences and perceptions of the instructors regarding the service training they received. Through in-depth interviews, participants shared insights into the effects of such training on their instructional methodologies, classroom practices, and overall professional development. Additionally, their perspectives on the perceived benefits, challenges, and effectiveness of the service training were examined. The findings shed light on the multifaceted impact of service training on English teachers, offering valuable insights into its influence on the teaching and learning process. This research contributes to a deeper understanding of the dynamics between professional development initiatives and educators’ beliefs and attitudes, providing implications for the design and implementation of future training programs aimed at enhancing EFL instruction. Findings emphasize the importance of aligning IST with teachers’ needs and fostering participatory involvement in its design.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".