What role can adaptive support play in an adaptable system?
Bibliographic record
Abstract
As computer applications become larger with every new version, there is a growing need to provide some way for users to manage the interface complexity. There are three different potential solutions to this problem: 1) an adaptable interface that allows users to customize the application to suit their needs; 2) an adaptive interface that performs the adaptation for the users; or 3) a combination of the adaptive and adaptable solutions, an approach that would be suitable in situations where users are not customizing effectively on their own. In this paper we examine what it means for users to engage in effective customization of a menu-based graphical user interface. We examine one aspect of effective customization, which is how characteristics of the users' tasks and customization behaviour affect their performance on those tasks. We do so by using a process model simulation based on cognitive modelling that generates quantitative predictions of user performance. Our results show that users can engage in customization behaviours that vary in efficiency. We use these results to suggest how adaptive support could be added to an adaptable interface to improve the effectiveness of the users' customization.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.022 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".