Resilience and Persistence: Unveiling the Journey of Arabic-Speaking Female Muslim Teachers in Adult ESL Education
Bibliographic record
Abstract
Teacher attrition is a global challenge, particularly in English as a Second Language (ESL) education for adults in Ontario, Canada, where diverse educators face systemic barriers undermining retention. Arabic-speaking female Muslim (ASFM) ESL teachers represent a critical yet underexplored group confronting specific challenges linked to complex interrelationships among race, language, religion, and gender. This study examines how the intersectional identities of ASFM ESL teachers shape and are shaped by their professional experiences, career trajectories, and decisions to remain in or leave the profession. Using a qualitative narrative case-study methodology, the research explores the experiences of five ASFM ESL teachers, purposively selected based on several criteria, including their completion of a Teaching English as a Second Language (TESL) program within the last seven years and age (30–50 years). Data collection included two semi-structured interviews and a checking-in session, addressing micro (personal experiences), meso (workplace interactions), and macro (sociopolitical contexts) levels. Analysis followed a four-phase process: transcription review, in vivo coding, thematic coding, and restorying and métissage. This study conceives teacher identity as multifaceted, dynamic, and socially constructed. Situating teacher identity within a critical hermeneutic framework provides nuanced insights into how intersecting identities and systemic barriers influence ASFM ESL teachers’ professional experiences, trajectories, and retention decisions. Emerging insights highlight four themes: persistent stereotypes and Islamophobia tied to visible markers like the veil; struggles for recognition and belonging; structural inequities perpetuated by native-speaker ideologies; and the burden of proving competency amidst accent and appearance-based discrimination. Despite systemic barriers, participants demonstrated resilience and agency, advocating for equity in their professional spaces. The findings of this study highlight the urgent need for institutional reforms to affirm ASFM ESL teachers’ identities, enhance retention, and foster inclusivity in educational spaces. In response, this study proposes the RRAAV model (Representation, Recognition, Acceptance, Acknowledgment, and Value) as a diagnostic framework for promoting equity in TESL programs and workplaces. Key recommendations include implementing structured mentorship, integrating cultural awareness training, and ensuring equitable hiring practices to better support marginalized educators. Looking ahead, future research should examine additional intersectional factors and assess the long-term impact of these interventions on teacher retention and professional growth.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| 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".