Bridging the theory and practice divide in pre-service language teacher education: A focus on teacher educators’ expertise from in-service teachers' vantage point
Bibliographic record
Abstract
The longstanding divide between theory and practice in language teacher education persists globally, resulting in many novice teachers feeling underprepared when they enter the field. Yet, few studies to date have examined how teacher educators may contribute to widening or bridging the gap; especially from the vantage point of in-service teachers. Unlike previous studies, this study uniquely explores 15 Korean in-service English teachers' perspectives on the expertise they feel pre-service English teacher educators should possess to help bridge the theory-practice divide. Data were collected through in-depth semi-structured interviews and were thematically coded using NVivo 14 software. Participants commonly mentioned that language teacher educators in South Korea often lack current classroom experience, leading to an emphasis on outdated theories irrelevant to contemporary teaching needs. They felt that this has led to a significant theory-practice divide in their teacher education, resulting in a disconnect between what is being taught and what teachers require when they enter the field. Participants suggest restructuring teacher education faculties to include educators who are knowledgeable in relevant research and theory, and also possess extensive up-to-date teaching experience in the types of classrooms their teacher-learners are likely to encounter. This study underscores the need to carefully consider language teacher educators’ qualifications and compositions in English teacher education programs to effectively bridge the theory-practice divide and better equip teacher-learners for their future teaching roles in South Korea and beyond.
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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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.006 |
| 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".