Building and Maintaining Self-Efficacy Beliefs: A Study of Entry-Level Vocational Education and Training Teachers
Bibliographic record
Abstract
New vocational education and training (VET) teachers in Quebec (Canada), as in other countries (e.g., France, the Netherlands, Norway, Sweden, Switzerland, the United States), face specific challenges and experience an atypical process of entry into the teaching profession. In addition to the known professional induction challenges, which requires numerous adjustments in terms of organizational integration and socialization as well as on a personal level, new VET teachers also experience a major professional and identity transition: they shift from experts in their field to novices in the teaching field. Moreover, as they are not generally trained in pedagogy, new teachers must enroll in a mandatory teaching bachelor's program once they are fully in charge of a class and its educational responsibilities. This complex process calls for a closer look at ways to facilitate their induction experience. Specifically, because it is closely related to motivation, engagement, and performance, this study focuses on their perceived self-efficacy as teachers. This study focused on 21 new VET teachers in the francophone province of Quebec, Canada, and aim to identify different strategies through which they succeeded in developing and maintaining their self-efficacy: strategies related to 1) the work of teaching, 2) mobilization of resources, 3) professional development, and 4) attitudes and well-being at work. These strategies allow us to gain a deeper understanding of previously unexplored aspects of the reality of these teachers, and to propose avenues for the development of interventions targeting their needs.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 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".