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Record W2783166888

Teachers' Perceptions of the Need for Assistive Technology Training in Newfoundland and Labrador's Rural Schools.

2017· article· en· W2783166888 on OpenAlexaboutno aff
Kimberly Maich, Tricia van Rhijn, Heather Woods, Kimberly Brochu

Bibliographic record

VenueThe Atrium (University of Guelph) · 2017
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsnot available
Fundersnot available
KeywordsVariety (cybernetics)PerceptionAssistive technologyPsychologyMedical educationNeeds assessmentTraining (meteorology)Identification (biology)MultimethodologyRural areaTechnology integrationEducational technologyPedagogyApplied psychologyMathematics educationMedicineComputer scienceGeographySociology
DOInot available

Abstract

fetched live from OpenAlex

This study examined the perceptions of teachers in rural Newfoundland (NL) about their current ability to support the use of assistive technology (AT) in their classrooms, and identified possible training needs that could be accomplished remotely. Thirty-two educators from rural
\nareas of NL completed an online survey with a mix of closed- and open-ended questions. Five dimensions were explored for this needs assessment including: current beliefs, skills, use, comfort level, and perceptions of AT; identification of specific service needs; learning preferences; available technology; and potential barriers. Results reveal that teachers had positive
\nattitudes about the utility and use of AT in their classrooms, yet were not fully implementing AT with their student’s due to a variety of perceived barriers. The study identified a clear need for AT teacher training. Based on the results and the research, literature recommendations are made for teacher training to address the need identified by this study.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.151
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.047
GPT teacher head0.345
Teacher spread0.297 · 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 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

Citations1
Published2017
Admission routes1
Has abstractyes

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