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Record W2293734850 · doi:10.1145/2839462.2854118

Designing Tangibles for Children

2016· article· en· W2293734850 on OpenAlexaff
Alissa N. Antle, Jillian L. Warren, Brendan Matkin, Min Fan, Emily S. Cramer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsSet (abstract data type)SituatedFidelityComputer sciencePsychologyProcess (computing)Focus groupDesign elements and principlesApplied psychologyMultimediaMedical educationHuman–computer interactionArtificial intelligence

Abstract

fetched live from OpenAlex

This hands-on workshop introduces a foundation for designing tangibles for children. Participants engage in a low-fidelity design challenge using the iPad Osmo system. We focus on how designing tangibles for children is unique from other design problems and processes. We walk participants through an outcome driven design process using the award winning Developmentally Situated Design (DSD) card set -- focusing on cognitive, emotional, physical, and social skills specific to children at different ages. Small groups create solutions for the same design challenge, but focus on the skills and abilities of a specific age group. We facilitate a compare and contrast exercise of their solutions to help synthesize the complexities of, and showcase skills for, designing child-centric tangibles. While not necessary for participation, we encourage participants who have them to bring iPads (v2 or higher) or iPad minis. Participants are also encouraged to review the DSD II cards in advance, available at http://www.antle.iat.sfu.ca/DSD.

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)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.018
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.003
Scholarly communication0.0030.004
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0180.003

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 head0.271
Teacher spread0.251 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations2
Published2016
Admission routes1
Has abstractyes

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