Supporting Novice to Expert Transitions in User Interfaces
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
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.
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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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