Multilevel analysis of teacher professional well-being and its influential factors based on TALIS data
Bibliographic record
Abstract
The current study examines how teachers’ professional wellbeing is affected by teacher-level and school-level factors using the TALIS 2018 data. Teacher-level factors consist of teachers’ instructional practices and teachers’ professional practices and school-level factors include school climate, school leadership styles and workload. The Hierarchical Linear Modeling (HLM) was used to examine whether the principals’ leadership, school climate and workload and teachers’ instructional practices and teachers’ professional practices explain the variation in teacher self-efficacy, teacher job satisfaction, and motivation and perceptions net of several important teacher-level and school-level control variables. The results revealed that both the teacher- and school-level factors were significantly related to teachers’ professional wellbeing. These findings were discussed concerning five countries of Canada, China, Finland, Japan and Singapore. The implications of the findings for improving teachers’ professional wellbeing are discussed.
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How this classification was reachedexpand
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 | 0.001 | 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) | 0.007 | 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 teacher head, 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".