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Record W2567054743

How Elementary School Teachers Adapt their Classroom Environment and Instructional Strategies in General Classroom Settings for Students with Visual Impairment

2014· article· en· W2567054743 on OpenAlexaboutno aff
Pamela Khaouli

Bibliographic record

VenueTSpace (University of Toronto) · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLeaf Properties and Growth Measurement
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationPsychologyPedagogyVisual impairment
DOInot available

Abstract

fetched live from OpenAlex

This qualitative study examines how elementary school teachers adapt their classroom environment and instructional strategies in general classroom settings for students with visual impairment (VI). Three elementary school teachers from district schools in the greater Toronto area and in Northern Ontario were interviewed about their experience teaching students with VI. The findings from the three interviews are presented in three case studies where four emergent themes are closely examined. A cross case analysis is also presented The findings suggest the importance of promoting a safe and inclusive learning environment where students with VI can learn in a positive, welcoming space and feel a sense of belonging. All three participants emphasized the importance of differentiated instruction by incorporating Gardner’s’ Theory of Multiple Intelligences. All participants made ongoing efforts to encourage students with VI to advocate for themselves in order to strengthen their educational independence. The findings from this research study and from the literature related to how students with VI learn suggest that teachers have made it a priority to establish inclusive learning environments, making the necessary accommodations to classroom materials and instructional strategies, and the use of assistive technology to support the needs and abilities for these students in general classroom settings.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.539
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.207
Teacher spread0.194 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2014
Admission routes1
Has abstractyes

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