Understanding teacher well-being and motivation : measurement, theory, and change over time
Bibliographic record
Abstract
Teacher well-being and motivation play important roles in teacher and student experiences at school. When teachers are faring well and feeling motivated to teach, they are more effective in their teaching, leave the profession less often, and promote motivation and achievement among their students. In this dissertation, three studies that investigated teacher well-being and motivation were conducted with the aim of advancing our understanding of the two constructs, as well as how they can be promoted among teachers. Study 1 involved conceptualising, developing, and testing the Teacher Well-Being Scale, which measures three factors of teacher well-being: workload well-being, organisational well-being, and student interaction well-being. Among a sample of 603 practicing teachers, results revealed that the new measure functioned similarly across the different demographic groups in the sample and that the three factors of well-being related as expected with other constructs (stress, job satisfaction, and flourishing). Study 2 involved elaborating and testing an explanatory model of teacher well-being, motivation, job satisfaction, and affective organisational commitment that was based in self-determination theory (Deci & Ryan, 1985, 2002). Using the same sample as Study 1, structural equation modelling provided support for the model’s main relationships. In addition, there were some unexpected findings that provide directions for future research (e.g., a double-sided view of autonomy revealing that it can be associated with positive and negative types of motivation). Study 3 involved examining growth curve models of change in teacher well-being and self-efficacy for teaching over two to three months. Among a sample of 71 practicing teachers, the findings showed that teacher well-being was stable over time, whereas self-efficacy for classroom management increased (the other two types of self-efficacy that were examined, self-efficacy for student engagement and instructional strategies, did not change over time). Findings also revealed the significance of the basic psychological needs (autonomy, competence, and relatedness) in predicting teacher well-being and self-efficacy. Taken together, the three studies help to improve our understanding of the highly important variables of teacher well-being and motivation. Implications of the findings for both research and practice 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 | 0.000 | 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) | 0.002 | 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 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".