Prediction of Users' Learning Curves for Adaptation while Using an Information Visualization
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
User performance and satisfaction when working with an interface is influenced by how quickly the user can acquire the skills necessary to work with the interface through practice. Learning curves are mathematical models that can represent a user's skill acquisition ability through parameters that describe the user's initial expertise as well as her learning rate. This information could be used by an interface to provide adaptive support to users who may otherwise be slow in learning the necessary skills. In this paper, we investigate the feasibility of predicting in real time a user's learning curve when working with ValueChart, an interactive visualization for decision making. Our models leverage various data sources (a user's gaze behavior, pupil dilation, cognitive abilities), and we show that they outperform a baseline that leverages only knowledge on user task performance so far. We also show that the best performing model achieves good accuracies in predicting users' learning curves even after observing users' performance only on a few tasks. These results are promising toward the design of user-adaptive visualizations that can dynamically support a user in acquiring the necessary skills to complete visual tasks.
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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 | 0.000 | 0.005 |
| Open science | 0.000 | 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