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Record W2075925848 · doi:10.1145/2675133.2675196

The Effects of View Portals on Performance and Awareness in Co-Located Tabletop Groupware

2015· article· en· W2075925848 on OpenAlexaff
David Pinelle, Carl Gutwin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsWorkspaceComputer scienceTask (project management)Human–computer interactionCollaborative softwareTable (database)Work (physics)Group (periodic table)World Wide WebMultimediaKnowledge managementEngineeringArtificial intelligenceDatabase

Abstract

fetched live from OpenAlex

Tabletop work surfaces have natural advantages for co-located collaboration, but also have physical constraints that can make group work difficult. View portals have been proposed as a way to provide access to other parts of a table surface, and as a way to re-orient content for group members in different locations; however, there is little research on whether portals really do improve group performance, how much they help, and whether they change other aspects of collaboration. We report on two studies that evaluate the effects of portals on group performance and behavior. Our first study showed significant performance advantages for portals: people were able to complete tasks more quickly and with more equal division of labor. Our second study, with a realistic design task, showed that people used portals extensively and saw them as valuable, but that they affected people's ability to maintain awareness, coordinate access to objects, and understand the organization of the workspace. Our work demonstrates benefits and potential drawbacks of portals for tables, and suggests that designers should carefully consider both individual and group needs before implementing these and other tabletop view augmentations.

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 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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.442
Threshold uncertainty score0.190

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 head0.016
GPT teacher head0.275
Teacher spread0.259 · 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 teacher head, 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

Citations3
Published2015
Admission routes1
Has abstractyes

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