English for Teacher Trainees: Increasing Motivation and Preparedness Through an ESP Workshop Design
Bibliographic record
Abstract
Many pre-service English as a Foreign Language (EFL) and Content and Language Integrated Learning (CLIL) teachers struggle with motivation and self-efficacy due to a lack of alignment between the English instruction they have received as learners and the specific linguistic and pedagogical demands of teaching EFL and CLIL. To address this, an Action Research (AR) project was conducted to redesign the English for Education II course at a Spanish university, shifting it from a General English to an English for Specific Purposes (ESP) approach with a workshop-centered methodology. Over four years, iterative cycles of planning, intervention, and evaluation guided the development of targeted workshops and reflective journal writing. Data from student surveys, focus groups, and instructor interviews indicate increased motivation, stronger perceived utility, and greater preparedness for pedagogical coursework and teaching practice. Findings highlight the benefits of ESP-based instruction for pre-service teachers and the role of collaborative AR in higher education curriculum innovation. This study underscores the need for English-for-Teaching courses in initial teacher education to bridge the gap between general language proficiency and professional language needs.
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 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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".