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Record W2106810011 · doi:10.1109/icme.2011.6012208

Mobile pointme based pervasive gaming interaction with learning objects annotated physical atlas

2011· article· en· W2106810011 on OpenAlex
A. Rahman, Abdulmotaleb El Saddik

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 institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceMetadataMobile deviceLeverage (statistics)Human–computer interactionAtlas (anatomy)AnnotationMultimediaWorld Wide WebArtificial intelligence

Abstract

fetched live from OpenAlex

The prevalent visions of ambient intelligence leverage natural interaction between user and available services in a learning space. In this pursuit, we propose a framework to facilitate handheld device based PointMe interaction with annotated media content, where the user points his/her handheld device to the annotated physical atlas for interacting with a world map. The proposed system performs annotations by specifying spatial location of the atlas and mapping related learning information to them. Each annotated data is encoded in customized Learning Object Metadata (LOM) format and they provide access points for available information about the specific countries in the map. This real world interaction technique with the physical environment and seamless virtual learning information acquisition make the system transparent from the young learners and help them to become engaged in their learning activities.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.818
Threshold uncertainty score0.404

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)0.0000.000

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 head0.021
GPT teacher head0.249
Teacher spread0.228 · 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
Published2011
Admission routes1
Has abstractyes

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