Won’t Stop, Can’t Stop: Alternative Route to Licensure Special Education Teachers’ Persistence in their Careers
Bibliographic record
Abstract
Alternate route to licensure (AR) programs in special education continue to increase despite concerns that teachers certified through these pathways leave the profession at rates higher than traditionally prepared teachers. The purpose of this study was to examine special education AR program completers to determine their persistence to stay in the profession despite odds of attrition. For this article, we examined survey results from AR special education teachers (n = 57) and completed focus group interviews with a subset (n =13) from this same sample. Using Social Cognitive Theory (SCT) to guide our research, we uncovered three major themes from our focus groups: role conceptualization, barriers experienced, and motivating factors. Our findings suggest that AR special education teachers’ persistence relies on several factors, such as society’s respect for teachers, effective mentoring programs, positive collaboration experience, understanding of their unique role as AR teachers, and self-efficacy. Implications for educational practices, policies, and further research about AR teachers is explored.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".