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Record W2137495373 · doi:10.1109/vlhcc.2008.4639104

Developing drawing and visual thinking strategies to enhance computer programming for people with dyslexia

2008· article· en· W2137495373 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

VenueProceedings/Proceedings -- IEEE Symposium on Visual Languages and Human-Centric Computing · 2008
Typearticle
Languageen
FieldComputer Science
TopicTeaching and Learning Programming
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDyslexiaComputer scienceHuman–computer interactionVisual thinkingMultimediaPsychologyMathematics educationReading (process)Linguistics

Abstract

fetched live from OpenAlex

Text based programming languages are difficult for dyslexics and many so-called learning disabled (LD) people to use [14]. However, weaknesses in mentally processing text-based prose associated with dyslexia and certain forms of LD often coincide with strong visual-spatial abilities [13, 12]. An informal participatory design inspired study revealed that a self-identified LD introductory programmer used hand-drawn spatial thinking techniques as a cognitive interface to a text-based programming interface. Using this and other clues as a starting point, my project seeks to continue this research through a more formal study by using experiences from a visually oriented LD student designer to develop a visual thinking and translation process that might enable the student and others to “draw their way” between their own (possibly spatial) runnable mental models and text-based programming environments.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.544
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0030.000
Scholarly communication0.0030.001
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 head0.014
GPT teacher head0.315
Teacher spread0.301 · 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