A model of novice and expert navigation performance in constrained-input interfaces
Bibliographic record
Abstract
Many interactive systems require users to navigate through large sets of data and commands using constrained input devices—such as scroll rings, rocker switches, or specialized keypads—that provide less power and flexibility than traditional input devices like mice or touch screens. While performance with more traditional devices has been extensively studied in human-computer interaction, there has been relatively little investigation of human performance with constrained input. As a result, there is little understanding of what factors govern performance in these situations, and how interfaces should be designed to optimize interface actions such as navigation and selection. Since constrained input is now common in a wide variety of interactive systems (such as mobile phones, audio players, in-car navigation systems, and kiosk displays), it is important for designers to understand what factors affect performance. To aid in this understanding, we present the Constrained Input Navigation (CIN) model, a predictive model that allows accurate determination of human navigation and selection performance in constrained-input scenarios. CIN identifies three factors that underlie user efficiency: the performance of the interface type for single-level item selection (where interface type depends on the input and output devices, the interactive behavior, and the data organization), the hierarchical structure of the information space, and the user's experience with the items to be selected. We show through experiments that, after empirical calibration, the model's predictions fit empirical data well, and discuss why and how each of the factors affects performance. Models like CIN can provide valuable theoretical and practical benefits to designers of constrained-input systems, allowing them to explore and compare a much wider variety of alternate interface designs without the need for extensive user studies.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".