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Visual Support for Use Case Modeling

2011· book-chapter· en· W2497892641 on OpenAlexaff
Andrew Gemino, Drew Parker

Bibliographic record

VenueIGI Global eBooks · 2011
Typebook-chapter
Languageen
FieldComputer Science
TopicData Visualization and Analytics
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsComputer scienceUse Case DiagramUnified Modeling LanguageFocus (optics)ComprehensionActivity diagramDe factoStory-driven modelingHuman–computer interactionClass diagramArtificial intelligenceProgramming languageSoftware

Abstract

fetched live from OpenAlex

The Unified Modeling Language (UML) has been evolving as a standard approach to Systems Analysis and Design. Use cases are a de facto standard tool, and corresponding use case diagrams offer visual support for this tool. The Cognitive Theory of Multimedia Learning suggests that the visual nature of use case diagrams would enhance understanding, particularly for novice users, by providing visual cues to focus relevant information. This paper describes an experiment to test this theory, offering use cases with and without supporting use case diagrams. Retention, comprehension, and problem solving tasks were tested and measured. As hypothesized, the results find that users had a significantly higher level of understanding, measured by problem solving tasks, if they were provided with use case diagrams accompanying the use cases. These results are promising support that use cases and use case diagrams could be considered important boundary objects in systems analysis.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.622
Threshold uncertainty score1.000

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 science0.0010.000
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 head0.079
GPT teacher head0.320
Teacher spread0.242 · 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.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations0
Published2011
Admission routes1
Has abstractyes

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