A multidimensional, person‐centred perspective on teacher engagement: Evidence from Canadian and Australian teachers
Bibliographic record
Abstract
BACKGROUND: Hitherto, little work has examined the proposition that teachers may simultaneously invest varying degrees of their energetic resources in the teaching and learning environment. AIMS: Drawing on a multidimensional, person-centred perspective, the study aimed to identify profiles of teacher engagement, reflecting distinct configurations of teachers' energetic investments, and their generalizability across Canadian and Australian teachers. Additionally, we examined teachers' self-efficacy beliefs as predictors of engagement profile membership and the cross-country generalizability of these relations. We also examined emotional exhaustion (EE) and job satisfaction (JS) as outcomes of profile membership in Canadian and Australian teachers, respectively. SAMPLES: The samples comprised 586 Canadian and 595 Australian teachers. METHODS: Data on teachers' multidimensional engagement and teacher self-efficacy were collected in both the Canadian and Australian samples. Additionally, data on Canadian teachers' burnout and Australian teachers' job satisfaction were collected. RESULTS: Latent profile analyses revealed three engagement profiles, representing distinct configurations of teachers' multidimensional energetic investments, which were found to mostly generalize. Additionally, self-efficacy beliefs were found to predict the likelihood of engagement profile membership equally in Canadian and Australian teachers, and EE in Canadian teachers and JS in Australian teachers were found to differ significantly across the profiles. CONCLUSIONS: The results of the study constitute among the first evidence that teachers may be differentially engaged in the teaching environment with respect to their distinct energetic investments, and such differential profiles of engagement have differential implications for well-being-related outcomes and can be predicted by their teaching capability beliefs.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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 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".