Relationships among teacher enjoyment, emotional labor, and perceived student engagement: A daily diary approach
Bibliographic record
Abstract
The present daily diary study among 587 Canadian primary and secondary school teachers assessed teachers' genuine expression, faking, hiding of happiness and enthusiasm, and their daily associations with perceived student emotional and behavioral engagement. Moreover, we measured teachers' trait enjoyment before and after the diary study to examine whether teacher trait enjoyment predicted the use of emotional labor strategies that, in turn, were related to teachers' perceptions of their students' engagement. In addition, we examined whether perceived student engagement predicted future levels of teacher trait enjoyment. Results from multilevel structural equation modeling showed that, at the between-person level, teachers who had higher levels of trait enjoyment tended to spontaneously show their positive feelings to their students (β = 0.381, p < .001), which was further positively related to student engagement (β = 0.257, p < .001). In turn, teachers' perceptions of heightened student engagement led to even greater enjoyment in the future (β = 0.134, p < .05). In contrast, teacher trait enjoyment was negatively related to faking (β = -0.297, p < .001) and hiding positive emotions (β = -0.130, p < .05), but was further unrelated to student engagement or future enjoyment. At the within-person level, genuine expression of positive emotions was positively related to student engagement (β = 0.219, p < .001), faking was negatively related to student engagement (β = -0.134, p < .001), and hiding was unrelated to student engagement.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".