Bibliographic record
Abstract
Adaptive interfaces have received much criticism because adaptation and automatic assistance generally contradict the principles of direct-manipulation interfaces. In addition, their success depends highly on the ability of user models to capture the goals and needs of the users. As the construction of user models is often based on poor evidence, even the most advanced learning algorithms may fail to infer accurately the user goals. Previous research has put little emphasis on investigating usability problems of adaptive systems and developing interaction techniques that could resolve these problems. This paper examines these problems and presents an interaction model for adaptive hypermedia (AH) that merges adaptive support and direct manipulation. This approach is built upon a new content adaptation technique that derives from fisheye views. This adaptation technique supports incremental and continuous adjustments of the adaptive views of hypermedia documents and balances between focus and context. By combining this technique with visual representations and controllers of user models, we form a twofold interaction model that enables users to move quickly between adaptation and direct control. Two preliminary user studies exhibit the strengths of our proposed interaction model and adaptation technique. Future extensions to our work are outlined based on the weaknesses and limitations that the studies revealed.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".