Nurturing Leadership in Language Teachers: Exploring the Why, What, and How in Pre-Service Education Programs
Bibliographic record
Abstract
Research in language teacher leadership has increased significantly over the past decade resulting in the emergence of various conceptualizations of what leadership entails at both the organizational and classroom levels. Although there is growing acknowledgment of the importance of language teachers being leaders inside and outside of the classroom, little empirical attention has been paid to how leadership qualities and characteristics can be integrated into and fostered through language teacher education programs. Thus, this qualitative exploratory study investigates stakeholders’ perspectives on the essential features of leadership in language teacher education in the South Korean context and how these features may be nurtured in teacher-learners in pre-service teacher education programs. Data were collected in two stages: first through semi-structured life-world interviews with 15 Korean public in-service secondary school English teachers and 15 English teacher educators in South Korea and then through follow-up interviews with the in-service teacher participant group. Findings suggest that essential language teacher leadership features include an integrated combination of possessing strong interpersonal skills, being contextually aware and situationally adaptive, and acting as a good language and learner model for students. Furthermore, in-service teachers felt that these essential features of leadership can be nurtured through interaction with knowledgeable peers, lesson observation and feedback, practicum and teaching experiences, and reflection. Based on these findings, we propose a conceptual model for nurturing essential leadership features in pre-service language teacher education programs that can be used as a point of reference when planning and designing language teacher leadership–focused seminars, workshops, courses, and programs in South Korea and wider global contexts.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".