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Record W2942511022 · doi:10.1145/3290607.3312756

Character Alive

2019· article· en· W2942511022 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
TopicEducation and Technology Integration
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsComputer scienceHandwritingCharacter (mathematics)SpellingWriting systemReading (process)Human–computer interactionChinese charactersLiteracyAugmented realityMultimediaArtificial intelligenceLinguisticsPsychology

Abstract

fetched live from OpenAlex

This paper presents Character Alive, a tangible system designed to improve early Chinese literacy acquisition for Mandarin-speaking children at-risk for dyslexia by enabling high-level interaction. Character Alive uses the multisensory training method to teach children the reading and writing of Chinese characters and words. The core design features of our system are augmented dynamic color cues, 2D radical cards and handwriting cards with tactile cues, and multimedia content such as character animations. Character Alive was built on our previous work on designing tangible and augmented reality reading and writing systems for children at-risk for dyslexia in English. Our previous work has demonstrated that dynamic color cues can draw children's attention to key characteristics of letter-sound-correspondences and two-hand actions with tangible letters help children to better solve spelling tasks. We present the design rationale, the design and implementation of the Character Alive system, and the future plan on evaluating the system.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.955
Threshold uncertainty score0.997

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.004

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 head0.016
GPT teacher head0.331
Teacher spread0.315 · 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

Citations18
Published2019
Admission routes1
Has abstractyes

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