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Towards a Tangible Blocky Coding Design for Visually Impaired Children

2022· article· en· W4313412876 on OpenAlex
Jennifer Alejandra Cardenas Castaneda, Patrick C. K. Hung, Farkhund Iqbal, Rafiq Ahmad

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
FieldComputer Science
TopicAugmented Reality Applications
Canadian institutionsOntario Tech UniversityUniversity of Alberta
Fundersnot available
KeywordsCoding (social sciences)Visually impairedComputer scienceVisualizationHuman–computer interactionEngineering design processInclusion (mineral)Learning environmentUniversal designMultimediaSoftware engineeringMathematics educationArtificial intelligencePsychologyEngineeringWorld Wide Web

Abstract

fetched live from OpenAlex

This paper investigates the role of coding in children's education and the barriers to inclusion for Visually Impaired (VI) children in this environment. In the learning process, these children face numerous challenges, including the fact that most playful coding-related learning options rely on visualization. This paper incorporates insights from a literature review to design a solution involving a model called DODO through computer vision and tangible paper blocky modules for VI children to learn to code. The main result is the proof of concept prototype demonstrates that the proposed design can recognize the various paper modules used to represent different codes, targeting the tactile sense. The design advocates for VI children to have access to economic Science, Technology, Engineering, and Mathematics (STEM) learning.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.931
Threshold uncertainty score0.414

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.036
GPT teacher head0.287
Teacher spread0.250 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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