Nonnative‐English‐speaking teacher candidates’ language teacher identity development in graduate TESOL preparation programs: A review of the literature
Bibliographic record
Abstract
This systematic review synthesizes 17 studies exploring nonnative‐English‐speaking teacher candidates’ (NNES‐TC) language teacher identity (LTI) development. The purpose was to examine NNES‐TCs’ LTI development during graduate‐level TESOL programs in the United States, Canada, and Australia. The review addressed the questions, What influences NNES teacher candidates’ LTI development, and in what ways do teacher preparation programs promote positive LTI development? Findings revealed four categories: (1) (non)native speakering and the native speaker fallacy, (2) racialized and gendered identities, (3) academic identity clashes, and (4) the emotional “glue” of LTI development. NNES‐TCs navigated personal and professional identities and struggled to balance their own expectations vis‐à‐vis expectations from their graduate programs and future teaching contexts. While native speakering discourses remained in claims of ownership over English, reflections on counter‐discourses and their connection to local teaching practices were agentively appropriated by NNES teacher candidates as they claimed legitimacy as teachers. Findings suggested teacher preparation should commit to critiquing native speakerism and offer spaces for empowering NNES‐TCs through narrative reflections. Preparation should explicitly address linguistic goals and needs, prioritize practical experience, and encourage teacher educators’ reflection on their practices. Future research should explore longitudinal LTI development, identities‐in‐practice, affective influences, and intersectional identities.
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".