Impact of Individual Differences on User Experience with a Real-World Visualization Interface for Public Engagement
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.
Bibliographic record
Abstract
There is increasing evidence that the effectiveness of Information Visualization (Infovis) is affected by the user needs and abilities. For instance, cognitive abilities (e.g., perceptual speed, working memory) [e.g., 1-4] have been shown to impact users' performance and satisfaction with a given visualization. These findings suggest that it can be valuable to develop visualization systems that can provide personalized support targeting specific user characteristics. Furthermore, recent research [e.g., 3,5] has shown that eye tracking data can be leveraged to identify the elements of a visualization for which specific user differences hinder user experience or performance, thus providing concrete information on which specific personalized support could be helpful for different users (e.g., users with low perceptual speed may benefit from help in processing legends [1]). Though these findings are encouraging toward the design of user-adaptive or customized visualizations, they are generally related to either fictional tasks or research prototypes. So, it is unclear if existing results on the value of user-adaptive visualizations can transfer to real-world settings.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it