Growing as we learn: Exploring the experiences of second-year beginning teachers
Bibliographic record
Abstract
Teaching is a demanding and challenging profession and thus beginning teacher attrition is likely during the induction phase. The cyclical turnover related to beginning teacher attrition has been demonstrated to have a significant negative impact on student achievement stretching beyond individual teachers and classrooms. This study examined beginning teachers' understanding of their work, expectations of their roles, and socialization experiences after successful completion of their second year in the teaching profession. Using a case study approach and a resiliency framework, semi-structured interviews with seven second year beginning teachers were conducted. Thematic analysis resulted in the identification of four major themes: (1) Evidence of Growth; (2) Support Systems; (3) Chalenges; and (4) Securing Placements. This presentation focuses on the theme of Evidence of Growth and the associated sub-themes of Changes Made and Workload Management. Participants reflected on how their teaching, especially with respect to classroom management (i.e., preventative techniques, discipline) and workload management evolved from their first tot heir second year of teaching. By examining the experiences of new teachers who have successfully completed their second year in the profession, our study will deepen and enrichunderstandings of key factors contributing to new teacher success and commitment to the profession.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".