New Teachers’ Career Intentions: Factors Influencing New Teachers’ Decisions to Stay or to Leave the Profession
Bibliographic record
Abstract
This study examines the relationship between the reported career intentions and perceptions of preparedness of graduating secondary teachers in Quebec, across a two- year period, in an effort to identify factors which contribute to growing attrition rates among beginning teachers. The study reveals that those beginning teachers most concerned with their lack of preparation in the areas of classroom management and assessment of students’ learning are more likely to consider leaving the profession. While evidence suggests that beginning teachers do develop increasing confidence in terms of classroom management in their second year of teaching, their challenges with effectively assessing student learning endure through the first two years of teaching. Findings from this mixed method study suggest that both initial teacher education and employers have a shared responsibility to give greater attention to the ways in which teachers are introduced to and have experience with strategies for the assessment of student learning. Cette étude porte sur le rapport entre les intentions de carrière et les perceptions qu’ont les finissants en enseignement secondaire au Québec quant à leur niveau de préparation. La recherche s’est étalée sur deux ans et visait à identifier les facteurs qui contribuent au taux grandissant d’attrition chez les enseignants débutants. L’étude a révélé que les enseignants débutants qui sont les plus préoccupés par leur manque de préparation en matière de gestion de classe et en évaluation des apprentissages sont également susceptibles de penser à quitter la profession. Bien que les résultats montrent que les enseignants débutants tendent à devenir plus confiants en gestion de classe pendant leur deuxième année d’enseignement, leur défis quant à l’évaluation des apprentissages persistent tout au long de leur deuxième année d’enseignement. Les résultats de cette étude qui reposent sur une méthode mixte indiquent également que, tant la formation initiale des enseignants que les employeurs, doivent porter attention à la présentation des notions relatives à l’évaluation des apprentissages et aux expériences qui sont offertes aux enseignants en début de carrière.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".