‘I felt a sense of panic, disorientation and frustration all at the same time’: the important role of emotions in reflective practice
Bibliographic record
Abstract
For many novice teachers, their first year on the job can be a roller coaster experience of ‘ups’ and ‘downs’ as they transition from their teacher education programs to teaching in real classrooms. While to ‘ups’ are always good to experience, the ‘downs’ can be so traumatic that novice teachers can feel so stressed that their teaching is adversely impacted and burned out to the point that they consider resigning for the profession. For the most part, however, the language teaching profession has not addressed this aspect of a novice ESL (English as a second language) teacher well-being in terms of their personal and emotional investment as they transition from trainee to novice teacher in their first year. This paper attempts to shed light on the emotional experiences of three female novice ESL teachers in a university language school in Canada as they reflected during regular group discussions and journal writing during their first semester (12 weeks) as novice ESL teachers. The results reveal that the group discussions and journal writing provided a platform for the teachers to articulate their mostly negative emotions with three most frequently expressed: frustration, anger and boredom.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".