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Record W2069721130 · doi:10.1109/digitel.2012.59

A Situated Game for Autistic Children Learning Activities of Daily Living

2012· article· en· W2069721130 on OpenAlexaff
Maiga Chang, Rita Kuo, Chunwei Lyu, Jia‐Sheng Heh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsAthabasca University
Fundersnot available
KeywordsSituatedAutismActivities of daily livingSet (abstract data type)PerceptionPsychologyComputer scienceMechanism (biology)Serious gameApplied psychologyPlan (archaeology)Human–computer interactionMultimediaDevelopmental psychologyArtificial intelligence

Abstract

fetched live from OpenAlex

Daily living skills are difficult for autistic children to learn because they have low motivation in learning new things. Some research has developed virtual environment to assist parents and teachers teaching autistic children daily living skills, educators still need to spend a lot of time in preparing personalized and more realistic tasks for children to practice. This research designs an activity generation mechanism by measuring activity's weight with fuzzy theory and rough set's help. Based on the activity generation mechanism and weight measurement, a Flash-based situated game is developed for providing autistic children personalized and non-repeated practices of activities of daily living. An evaluation plan of the pilot for verifying the effectiveness of the game and gathering the users' (include teachers, parents, and the autistic children) perceptions toward the game and the game-play is designed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.033
GPT teacher head0.304
Teacher spread0.272 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2012
Admission routes1
Has abstractyes

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