IMMIGRANT NON-NATIVE ENGLISH SPEAKING TEACHERS IN TESOL: THE NEGOTIATION OF PROFESSIONAL IDENTITIES
Bibliographic record
Abstract
This study examined how immigrant non-native English speaking teachers (INNESTs) in the field of TESOL (Teachers of English to Speakers of Other Languages) negotiate professional identities within the framework of a TESOL certification program. Three aspects of their identities were investigated: language-related identity, disciplinary identity, and teaching self-knowledge. Two groups of participants were recruited. The first group (115 in number) had already completed a TESOL program and were ESL teachers. The second group (five in number) were all registered in a full-time TESOL program to become certified to teach ESL in Ontario, Canada. A complementary, concurrent component mixed methods research design was adopted. The quantitative and qualitative data were collected through various data collection strategies. The analysis is informed by a number of theoretical concepts: identity, critical pedagogy, sociocultural theory, and teacher cognition. The main findings are as follows. First, INNESTs viewed the native speaker construct as consisting of multiple dimensions. The findings suggest that the construct is complex, contextual, dialogic, dynamic, ideological, intersecting, multifaceted, negotiable, relational, situated, and shifting as a developmental process as opposed to a fixed unitary state. Despite that, INNESTs continue to experience mostly conventional realizations of the construct. Second, institutional certification serves as a symbolic discourse of de-professionalization for INNESTs applying for certification. INNESTs reacted to such discourse in different ways, accepting and internalizing or questioning and resisting it. The certification process did not distinguish the experienced INNESTs from their novice native-born teacher candidates; but it did provide them with recognition, acceptance, and legitimacy in the professional TESOL community. Third, the majority of the INNESTs reported a high level of confidence by presenting a generally positive self-image. The findings highlight the importance of self-image in negotiating teaching knowledge in the TESOL community. Moreover, INNESTs’ cognition is influenced by conflicts they experience reconciling their new teaching context, their past learning experiences and teaching beliefs, as well as their instructional decisions. Implications regarding ways to facilitate positive identity construction and professional integration are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".