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Record W1948583394 · doi:10.1002/asi.23470

Intuitive or idiomatic: An interdisciplinary study of child‐tablet computer interaction

2015· article· en· W1948583394 on OpenAlexaff
Rhonda McEwen, Adam K. Dubé

Bibliographic record

VenueJournal of the Association for Information Science and Technology · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsRoche (Canada)University of Toronto
Fundersnot available
KeywordsVariety (cybernetics)Scope (computer science)Computer scienceHuman–computer interactionMultimediaArtificial intelligence

Abstract

fetched live from OpenAlex

Using Luhmann's communication framework, we examine the interaction implications for kindergarten to Grade 2 students using mathematics applications on four types of tablet computers. Research questions included what content is communicated between the child and the tablet computer and how engaged are children in the interaction. We found that mathematics applications developers have focused on creating applications for the practice of a priori knowledge, rather than on creating instructional applications. Results show preliminary evidence that child‐tablet communication is generally successful, but this success comes at the cost of richer, multimodal interactions. Tablet computer application developers are being cautious in offering a variety of options for children to interact with the devices, and we suggest that there is scope for a broadening of communicative interaction modes.

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)0.005
metaresearch head score (Gemma)0.015
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0040.009
Scholarly communication0.0060.004
Open science0.0010.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.353
Teacher spread0.325 · 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

Citations18
Published2015
Admission routes1
Has abstractyes

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