Bibliographic record
Abstract
Use case modeling in the Unified Modeling Language (UML) is a popular text-based tool for systems analysis and design. Use cases can be used with or without supporting use case diagrams. This paper uses an experiment to explore the effectiveness of including a use case diagram with a set of use cases. The Cognitive Theory of Multimedia Learning is used to hypothesize that the use case diagram improves the effectiveness of use cases for novice users by providing visual cues aiding model viewers in selecting and integrating relevant information. The level of understanding developed by participants viewing either uses cases or use cases with a supporting use case diagram was measured using comprehension, retention, and problem solving tasks. Results showed that participants viewing the use cases with the supporting diagram developed a significantly higher level of understanding, as measured by performance on the problem solving task, than participants provided with use cases alone. This analysis suggests practitioners should consider combining a visual representation, such as a use case diagram, with text-based use cases to achieve higher levels of understanding in persons viewing these descriptions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.057 | 0.168 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".