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Record W1747676963 · doi:10.5539/jel.v4n3p71

Teachers’ Aides Working in Secondary School Settings: Preparedness and Professional Learning

2015· article· en· W1747676963 on OpenAlexvenueno aff
Dianne Gibson, Louise Paatsch, Dianne Toe, Muriel Wells, Shaun Rawolle

Bibliographic record

VenueJournal of Education and Learning · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicCollaborative Teaching and Inclusion
Canadian institutionsnot available
Fundersnot available
KeywordsPreparednessPsychologyProfessional developmentPedagogySecondary educationMedical educationMathematics educationMedicinePolitical science

Abstract

fetched live from OpenAlex

In Victoria, Australia teachers’ aides (TAs) are employed to provide support to students with disabilities in accessing their education. The role of the TAs varies within and across school settings. Drawing from the findings of a quantitative study, the purpose of this paper is to develop an understanding of teachers’ aides’ perceptions of their preparedness to perform 18 student-related tasks within the state secondary school setting in Victoria, Australia. In all, 163 participants completed the on-line questionnaire. The results of the study showed that that in general TAs perceive there are tasks relevant to their roles in supporting students with disabilities; and there are tasks that are not applicable to that role. The TAs in this study considered that they had training that enabled them to effectively perform the listed student related tasks to support students with disabilities in the secondary school environment.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score0.263

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.003
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
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 head0.025
GPT teacher head0.362
Teacher spread0.336 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations5
Published2015
Admission routes1
Has abstractyes

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