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Record W4290022654 · doi:10.1177/0145482x221109561

Using Appropriate Assessment to Plan Braille Literacy Instruction

2022· article· en· W4290022654 on OpenAlexaff
R. K. Stewart, Kim T. Zebehazy, M. Cay Holbrook

Bibliographic record

VenueJournal of Visual Impairment & Blindness · 2022
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBrailleLiteracyPsychologyReading (process)RhymeIntervention (counseling)Mathematics educationPerceptionPhonicsComputer sciencePedagogyPrimary educationLinguistics

Abstract

fetched live from OpenAlex

To be effective teachers of literacy for students who read braille, we need to know what our students’ strengths and needs are as all these components come together in braille literacy. Using an appropriate assessment that addresses the skill sets involved in braille literacy is critical to putting together effective intervention packages for our students. The first author used the Kamei-Hannan and Ricci Reading Assessment (2015) and the Braille Reading Analysis Chart (Harley, et al., 1997) to determine areas of need for a student in grade 2 in a braille literacy program. Needs included: identifying ending sounds and naming final letters and sounds; basic decoding of short and long vowel sounds; recognizing sight words; and identifying letters. Specific miscue patterns in tactile perception (reversals) were identified. Information gathered from these assessments was used to develop a targeted intervention package and informed the development of a balanced literacy program for this student. Following a period of implementation, data showed evidence of overall improvement in braille literacy skills.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.307
Threshold uncertainty score0.612

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.080
GPT teacher head0.409
Teacher spread0.329 · 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 designBench or experimental
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

Citations2
Published2022
Admission routes1
Has abstractyes

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