Thriving on Challenge: Examining One Teacher’s View on Sources of Support for Motivation and Well-Being
Bibliographic record
Abstract
Alarmingly high rates of teacher attrition exist in contexts designed for students with considerable needs, such as in alternative education programs serving marginalized youth. Research has linked teachers’ levels of motivation and well-being to their effectiveness and retention. Consequently, we explore what distinguishes teachers who thrive in contexts others find taxing. Specifically, we investigate whether and how their motivation and well-being support their teaching effectiveness. As part of a larger case study of an alternative education program for youth who haven’t found success in mainstream schools, this article reports a semi-structured interview asking whether and how one teacher’s perceived autonomy, belonging, and competence support other facets of his motivation (e.g., teaching efficacy) and his well-being (i.e., constructive responses to potentially stressful events.) Plentiful evidence was found to link our researcher-derived constructs from self-determination theory to the teacher’s professional experiences in general, and to his work with youth in particular, indicating that our conceptual framework is an authentic representation of his experience. Implications for theory and research are discussed.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".