Investigating Why Alternatively Prepared Special Educators Frequently Depart the Classroom
Bibliographic record
Abstract
AbstractA quarter of special education teachers who have been trained through an alternative teacher preparation program have left assigned classrooms throughout the United States after 1 year, and almost half have left within 5 years. However, little is known regarding why special educators, alternatively prepared for the classroom, leave the classroom after 2-5 years of classroom experience. The purpose of this basic qualitative study was to understand why special education teachers enter school districts through alternative teacher certification programs but exit the classroom. The conceptual framework for this study was in the societal theory attributed to Maslow’s hierarchy of needs. The research questions inquired how former alternatively trained special educators described the reasons for leaving the classroom, and how school administrators describe the reasons special education teachers trained through alternative certification programs leave the classroom. Data were collected through semistructured interviews with 20 special educators and 10 school administrators. Five themes regarding special educators’ rationale for leaving were lack of support, overwhelming caseloads, an abundance of paperwork, not being properly trained, and student behavior. Policymakers and district leaders may be able to use the results of this study to guide and develop policies that address the increasing special education teacher shortage. These findings bear the potential to generate positive social change by assisting decision-makers on what resources and supports school districts would need to recruit and retain a diverse workforce of special educators.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".