Reducing Teacher Burnout by Increasing Student 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
Teacher burnout has long been understood to have significant negative effects on teaching efficacy. Research has indicated that student misbehaviour, often a result of disengagement, is a major predictor of teacher burnout. In part to address student disengagement, Hampshire County in England has undertaken a whole-school rights-based reform initiative called Rights, Respect and Responsibility (RRR). This study was designed to examine the effects of RRR on student engagement and teacher burnout over a three-year period. The sample initially comprised a total of 15 schools (four infant, five primary and six junior) and 127 teachers. At the second time of measure, one year later, the sample was reduced to 69 teachers from 13 of the schools. At both times teachers completed the following measures: the Maslach Burnout Inventory, the perceived effect of RRR on teaching, and student engagement. In the third year of the study we obtained data on the Maslach Burnout Inventory from 100 teachers at 12 of the schools. Findings suggest that RRR can improve student engagement and reduce teacher burnout. Of particular note was the predictive power of student participation in the classroom and school in reducing teacher burnout.
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 | 0.000 |
| 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) | 0.006 | 0.001 |
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