Interactivity in Visual Analytics: Use of Conceptual Frameworks to Support Human-Centered Design of a Decision-Support Tool
Bibliographic record
Abstract
Visual analytics (VA) combines the strengths of humans and computers such that joint cognitive systems are formed. To be effective, a VA tool should be designed such that the component parts of the whole system are strongly coupled and function in a harmonious fashion. These components include cognitive and perceptual issues, tasks, algorithms, data models, and other aspects of the systems that contribute to its overall efficacy. The quality of interaction among all of these components can be referred to as interactivity. In the existing visualization literature, not enough focus has been placed on developing our understanding of human-centered aspects of interactivity. We have recently developed some conceptual frameworks to inform and guide the design of visual analytics tools in a systematic, human-centered fashion. In this paper, we describe the design of a tool that supports decision-making and other complex cognitive activities. We discuss how the conceptual frameworks supported systematic design and coherent thinking about the interactivity of the tool. We also discuss some extensions of interactivity into important areas of concern for visual analytics tools.
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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 | 0.025 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".