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Record W2005918404 · doi:10.3402/rlt.v1i1.9471

Recognition of animated icons by elementaryaged children

2011· article· en· W2005918404 on OpenAlexaff
Tom Jones

Bibliographic record

VenueResearch in Learning Technology · 2011
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsComputer sciencePreferenceInterpretation (philosophy)Action (physics)Human–computer interactionAnimationMultimediaPsychologyVisual artsComputer graphics (images)ArtProgramming language

Abstract

fetched live from OpenAlex

This paper describes a study to investigate the recognizability of and preference for animated icons by elementary-aged aged children. Fourteen typical computer-related tasks (e.g., copy, move) were viewed by 60 school-children in two iconic formats: animated and static. The content of the icons and the computer process or action they mimicked were drawn from a previous study in which a similar group of children was asked to depict gesturally their interpretation of the 14 tasks. Results indicated that the animated version of the icons was more recognizable and that the children greatly preferred the animated icons over the static icons. Implications for the design of enhanced user-interfaces for children are noted.DOI:10.1080/0968776930010105

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.087
GPT teacher head0.351
Teacher spread0.264 · 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 designObservational
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

Citations10
Published2011
Admission routes1
Has abstractyes

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