Is teacher humor an asset in classroom management? Examining its association with students’ well-being, sense of school belonging, and engagement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it