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Record W2147785357 · doi:10.3991/ijet.v6is2.1640

An Assistive Computerized System with Tangible User Interfaces for Children with Moderate Intellectual and Learning Disabilities

2011· article· en· W2147785357 on OpenAlexaff
Jihad Mohamad Alja’am, Ali Jaoua, Saleh Alhazbi, Ahmad Hasnah, Ali Karime, Abdelmutalib A. Elsaddik

Bibliographic record

VenueInternational Journal of Emerging Technologies in Learning (iJET) · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Accessibility for Disabilities
Canadian institutionsUniversity of Ottawa
FundersQatar UniversityQatar Foundation
KeywordsMemorizationComputer scienceHuman–computer interactionMultimediaProcess (computing)CreativityComponent (thermodynamics)Special needsPsychologyMathematics education

Abstract

fetched live from OpenAlex

In this paper we propose an assistive learning system for children with moderate intellectual and learning disabilities that supports collaboration, data exploration, communication and creativity. The system offers a wide range of tutorials on basic concepts of elementary sciences with some edutainment games and puzzles based on different tangible user interfaces TUIs. The system can enhance the communications, memorization, reasoning and learning capabilities of the children with special needs. The tutorials contain multimedia elements that help the children understand effectively the topics and allow them to interact and be more proactive. An assessment component is developed to evaluate the children understanding. Parents are actively involved in the learning process by being able to add or customize contents specific to their children. The children can use the TUIs alone and get prompted on all the steps to perform some daily activities like the school day activity, the tooth brushing activity, etc. This will increase their self-reliance and self-dependence.

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)0.001
metaresearch head score (Gemma)0.002
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: none
Teacher disagreement score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0120.002

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 head0.027
GPT teacher head0.301
Teacher spread0.274 · 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".

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Citations5
Published2011
Admission routes1
Has abstractyes

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Same venueInternational Journal of Emerging Technologies in Learning (iJET)Same topicDigital Accessibility for DisabilitiesFrench-language works237,207