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Record W2344484630 · doi:10.1109/tla.2015.7404939

Using Design Patterns as Usability Heuristics for Mobile Groupware Systems

2015· article· en· W2344484630 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.

Bibliographic record

VenueIEEE Latin America Transactions · 2015
Typearticle
Languageen
FieldPsychology
TopicTeam Dynamics and Performance
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsUsabilityHuman–computer interactionComputer scienceCollaborative softwareHeuristicsUser interfaceHeuristic evaluationComputer-supported cooperative workPerspective (graphical)Usability labUsability engineeringKnowledge managementEngineering

Abstract

fetched live from OpenAlex

The objective of this research was to determine the capability of design patterns to find usability issues in mobile groupware interfaces. A particular collection of patterns was used for this purpose. Patterns were obtained by identifying essential activities and collaborative tasks in the communication process among members of working groups. A previous study on said proposal suggested that design solutions offered by the patterns foster communication, collaboration, and coordination through user interface elements. This new analysis evaluated virtues of the proposal from a user interface-assessment perspective. For this purpose 6 experts on design, usability, HCI and UCD validated the heuristics, which were then applied by 12 members of an educational support group called USAER. Results suggested that proposed heuristics could provide a reliable perspective of the usability level of mobile user interfaces for groupware applications.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.843
Threshold uncertainty score0.719

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 head0.145
GPT teacher head0.378
Teacher spread0.233 · 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