Is teacher humor an asset in classroom management? Examining its association with students’ well-being, sense of school belonging, and engagement
Bibliographic record
Abstract
Abstract This study used the instructional humor processing theory to test how different humor subtypes employed by teachers (course-related, course-unrelated, self-disparaging, other-disparaging) relate to students’ well-being, sense of belonging, and engagement. The participants comprised 395 students (boys = 106; girls = 270; other = 8; NA = 11) (secondary school students = 291; primary school students = 97, NA = 7) from five public school boards located in rural areas, and one private secondary school situated in an urban area (M age = 14.11) with a proportion of 93% speaking French at home. Correlational and structural equation modeling methods were used to analyze these relationships. Results showed that only humor related to course content (positive association) and other-disparaging humor (negative association) were significantly associated with the sense of belonging, which, in turn, was positively associated with a cognitive, affective, and behavioral engagement. Results also showed that only course-related humor (positive association) and unrelated humor (negative association) were significantly associated with students’ emotional well-being, which, in turn, was positively associated with cognitive and affective engagement. As far as this study is concerned, humor in the classroom should be course-related when it comes to supporting students’ emotional well-being, sense of belonging, and 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 | 0.002 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".