Supporting Novice to Expert Transitions in User Interfaces
Bibliographic record
Abstract
Interface design guidelines encourage designers to provide high-performance mechanisms for expert users. However, research shows that many expert interface components are seldom used and that there is a tendency for users to persistently fail to adopt faster methods for completing their work. This article summarizes and organizes research relevant to supporting users in making successful transitions to expert levels of performance. First, we provide a brief introduction to the underlying human factors of skill acquisition relevant to interaction with computer systems. We then present our focus, which is a review of the state of the art in user interfaces that promote expertise development. The review of interface research is based around four domains of performance improvement: intramodal improvement that occurs as a factor of repetition and practice with a single method of interaction; intermodal improvement that occurs when users switch from one method to another that has a higher performance ceiling; vocabulary extension , in which the user broadens his or her knowledge of the range of functions available; and task mapping , which examines the ways in which users perform their tasks. The review emphasizes the relationship between interface techniques and the human factors that explain their relative success.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".