Understanding Emotional Well‐Being and Self‐Directed Professional Development of Language Teachers in a Private School: An Ecological Perspective
Bibliographic record
Abstract
ABSTRACT Adopting a nested model of ecological systems, this study investigated how language teachers working in a private elementary school in Turkey experienced their emotional well‐being and what factors affected their emotional well‐being and self‐directed professional development. A biodata questionnaire, semi‐structured interviews, and journal writing were leveraged to identify the participants’ dynamic changes in their well‐being and professional development. The language teachers’ emotional well‐being and their teaching experiences were qualitatively analyzed under four categories namely, micro‐, meso‐, exo‐, and macrosystems of the nested ecosystem model. A grounded theory approach was used for the qualitative analysis, and the emergent codes were compared to reveal the dimensions of the dynamic ecological changes. The findings of the study provided evidence to support the dynamically changing trajectories and variables in language teachers’ emotional well‐being and their self‐directed professional development related to individual and contextual factors, namely feeling emotionally depleted and on edge in a volatile world, precarious employment, unstable schedules, and parental pressure eclipsing teacher roles. However, the sense of cooperation and collegiality among the language teachers allowed them to cope with the challenges and empowered them to tap into their professional career goals. The findings of this study contribute to the knowledge of language teachers’ well‐being and resilience in their instructional environment and provide implications for future research for language teacher professional development.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".