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Record W1982954775 · doi:10.1145/2677199.2688806

Tactile Letters

2015· article· en· W1982954775 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Accessibility for Disabilities
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsComputer scienceHuman–computer interactionTexture (cosmology)Interface (matter)DyslexiaSpace (punctuation)Learning to readMultimediaArtificial intelligenceReading (process)LinguisticsImage (mathematics)

Abstract

fetched live from OpenAlex

Dyslexic children have great difficulty in learning to read. While research in HCI suggests that tangible user interfaces (TUIs) have the potential to support children learning to read, few studies have explored how to help dyslexic children learn to read. Even fewer studies have specifically investigated the design space of texture cues in TUIs in supporting learning to read. In this paper, we present Tactile Letters, a multimodal tangible tabletop with texture cues developed to support English letter-sound correspondence learning for dyslexic children aged 5-6 years old. This prototype is used as a research instrument to investigate the role of texture cues in a multimodal TUI in alphabetic learning. We discuss the current knowledge gap, the theoretical foundations that informed our core design strategy, and the subsequent design decisions we made while developing Tactile Letters.

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.611
Threshold uncertainty score0.575

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.090
GPT teacher head0.358
Teacher spread0.268 · 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

Quick stats

Citations19
Published2015
Admission routes1
Has abstractyes

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Same topicDigital Accessibility for DisabilitiesFrench-language works237,207