Teacher turnover intention: a Social Cognitive Career Theory perspective
Bibliographic record
Abstract
The rate at which beginning teachers are leaving the profession is of concern in Australia and internationally (Plunkett & Dyson, 2011; Toropova et al., 2021). Teacher turnover affects the individual teachers, their students, and the school systems that they exit, with higher turnover rates negatively impacting on student academic outcomes and creating an additional financial burden of recruiting and inducting new teachers (Borman & Dowling, 2008; Sorensen & Ladd, 2020). Teacher turnover intention estimates a teacher's desire to remain in the teaching profession. Work engagement, job satisfaction, and life satisfaction have been found to influence turnover intention in other professions; however, their combined predictive role in teacher turnover was unclear (Amah, 2009; Dreer, 2021b; Ghiselli et al., 2001; Williams, 2011). The Social Cognitive Career Theory (SCCT; Brown & Lent, 2019; Lent & Brown, 2019) well-being model provided the theoretical framework by which to investigate the relationships among personality, contextual, social, and cognitive variables, and their predictive value for work engagement, job satisfaction, and life satisfaction. The aims of this research project were firstly, to investigate to what extent the SCCT well-being model is able to explain teacher work engagement, job satisfaction, and life satisfaction; and secondly, to investigate the relationships between work engagement, job satisfaction, and life satisfaction and teacher turnover intentions. The SCCT well-being model was operationalised with variables relevant to the domain of the teaching profession, including openness, conscientiousness, extraversion, agreeableness, neuroticism, dispositional optimism, perceived organisational support, teaching self-efficacy, vocational outcome expectations, work engagement, teaching satisfaction, and life satisfaction. Surveys were deployed to obtain measures of the operationalised variables in two studies. The Study 1 sample (N = 371) included preservice teachers enrolled in teacher education programs at a regional Queensland university and in-service Australian teachers. Study 2 participants (N = 394) were teachers from Australia, Canada, New Zealand, Northern Ireland, the United Kingdom, and the United States of America. The SCCT well-being model provided a testable framework for investigating the predictors of teachers' work engagement, job satisfaction, and turnover intention, accounting for 45.7% and 58.5% of the variance in work engagement, 62.3% and 47.3% of the variance in job satisfaction, and 45.1% and 41.9% of the variance in teachers' life satisfaction in Studies 1 and 2, respectively. Teachers' job satisfaction accounted for 13.0% and 26.7% of the unique variance in turnover intentions in Studies 1 and 2 respectively. Work engagement and life satisfaction, whilst correlated with turnover intention, did not account for any unique variance in turnover intention in the final models of the sequential multiple regression analyses. The findings from this research contribute to the teacher turnover intention literature, and suggest a number of practical implications that universities, schools, and education centres can employ to increase teachers' work engagement, job satisfaction, life satisfaction, and intention to remain in the profession. These interventions include increasing teacher's positive affect at work through positive psychological interventions, increasing teaching self-efficacy through professional development activities such as scenario-based learning, and increasing teacher's perceived organisational support through mentoring programs, and offering teachers ongoing employment contracts. Whilst the cross-sectional design of the study did not provide evidence for causal links among the variables, the SCCT well-being model provided a testable, theorised order of constructs that can be contextualised for specific professions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".