MétaCan
Menu
Back to cohort
Record W7048017408

Investigating Why Alternatively Prepared Special Educators Frequently Depart the Classroom

2022· article· en· W7048017408 on OpenAlexaboutno aff

Bibliographic record

VenueScholarWorks (Walden University) · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSuperconducting and THz Device Technology
Canadian institutionsnot available
Fundersnot available
KeywordsAlternative teacher certificationSpecial educationCertificationWorkforceTeacher educationQualitative researchQuarter (Canadian coin)HierarchySpecial Interest GroupMultimethodology
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.762
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.016
GPT teacher head0.224
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueScholarWorks (Walden University)Same topicSuperconducting and THz Device TechnologyFrench-language works237,207