Satisfied teachers are good teachers: The association between teacher job satisfaction and instructional quality
Bibliographic record
Abstract
Abstract Instructional quality is associated with better academic outcomes for students. This study aimed to investigate how teachers' job satisfaction was associated with clarity of instruction and cognitive activation as measures of instructional quality. In addition, we investigated whether this association between teachers' job satisfaction and instructional quality was mediated by teacher–student relationships. Drawing on the 2018 Teaching and Learning International Survey (TALIS), we compared participants from both Eastern ( N = 27,106; Japan, Taipei, Korea, Shanghai) and Western sociocultural contexts ( N = 20,209; Canada, Australia, New Zealand, United States, United Kingdom). Multilevel structural equation modelling results indicated that teachers' job satisfaction was positively associated with instructional quality across Eastern and Western settings. The relationship between teachers' job satisfaction and instructional quality was partially mediated by better student–teacher relationships. There were some differences between the cultural settings in how job satisfaction correlated with clarity of instruction and cognitive activation. We suggest that these differences may be accounted for by cultural characteristics leading to different approaches to teaching. Our results suggest that teachers' job satisfaction and the quality of classroom‐level relationships may be important indicators of positive instructional outcomes. While schools focus on student outcomes, they should also address teachers' job satisfaction and prioritise the importance of relationships between teachers and students in classrooms.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".