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Record W2005168038 · doi:10.1145/1551788.1551803

Hands on what?

2009· article· en· W2005168038 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsSimon Fraser University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsJigsawTask (project management)Computer scienceHuman–computer interactionObject (grammar)Task analysisArtificial intelligenceCognitive psychologyPsychologyMathematics education

Abstract

fetched live from OpenAlex

We investigate the similarities and differences -- in terms of quantitative performance and qualitative behaviors -- between how children solve an object manipulation task using mouse-based input versus tangible-based input. This work examines the assumption common in tangible computing that direct physical manipulation is beneficial for certain spatial tasks. We describe an ecologically valid comparison of mouse-based versus tangible-based input for a jigsaw puzzle task in order to better understand the tradeoffs in choosing input and interaction styles. We include a traditional cardboard puzzle for comparative purposes. The results of an experiment with 132 children indicate children are more successful and faster at solving puzzles using a tangible-based approach. Detailed temporal analysis indicates that pairs in the tangible group spend most of their time using a combination of epistemic and pragmatic actions which support mental problem solving. Conversely, pairs in the mouse group use an ineffective trial and error strategy.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.942
Threshold uncertainty score0.804

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.010
GPT teacher head0.258
Teacher spread0.248 · 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

Citations80
Published2009
Admission routes2
Has abstractyes

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