Defining Recommendations to Guide User Interface Design: Multimethod Approach
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
BACKGROUND: For the development of digital solutions, different aspects of user interface design must be taken into consideration. Different technologies, interaction paradigms, user characteristics and needs, and interface design components are some of the aspects that designers and developers should pay attention to when designing a solution. Many user interface design recommendations for different digital solutions and user profiles are found in the literature, but these recommendations have numerous similarities, contradictions, and different levels of detail. A detailed critical analysis is needed that compares, evaluates, and validates existing recommendations and allows the definition of a practical set of recommendations. OBJECTIVE: This study aimed to analyze and synthesize existing user interface design recommendations and propose a practical set of recommendations that guide the development of different technologies. METHODS: Based on previous studies, a set of recommendations on user interface design was generated following 4 steps: (1) interview with user interface design experts; (2) analysis of the experts' feedback and drafting of a set of recommendations; (3) reanalysis of the shorter list of recommendations by a group of experts; and (4) refining and finalizing the list. RESULTS: The findings allowed us to define a set of 174 recommendations divided into 12 categories, according to usability principles, and organized into 2 levels of hierarchy: generic (69 recommendations) and specific (105 recommendations). CONCLUSIONS: This study shows that user interface design recommendations can be divided according to usability principles and organized into levels of detail. Moreover, this study reveals that some recommendations, as they address different technologies and interaction paradigms, need further work.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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