Pattern-oriented UI design based on user experiences : a method supported by empirical evidence
Bibliographic record
Abstract
User-Centered Design (UCD) is a philosophy surrounding interactive system design, with the purpose of achieving product usability. One challenge with UCD and its related methods is the lack of a concrete process which supports designers in building user interface (UI) designs founded on user experiences. In current practice, design decisions are made based on loosely-defined guidelines, giving rise to a significant "gap" between user analysis and design outcomes. This is especially problematic for novice designers who lack the background and training required to make trade-offs, judgments and interpretations towards a usable design. In this thesis, we propose a Pattern-Oriented UI Design method which is driven by user experiences. It is founded on a set of core UCD principles which we have enriched with "engineering-like" concepts such as reuse and traceability. The method is based on two key artifacts--personas, used to model user experiences, and patterns, used to capture best design practices. Following this method, we define the UX-P Process, a systematic process which is semi-automated and characterized by rigorously-defined steps; designers iteratively create personas, select patterns, and compose patterns into a comprehensive design, based on user specifications and usability considerations. We have built a supporting tool, which allows designers to cluster users into personas and select candidate patterns based on persona specifications. We carried out two empirical studies with end-users. The goal of the first study was to assess the feasibility of the method; the second, to validate the process. Both studies were carried out with Bioinformatics applications and were comparative in nature testing the original design with our prototype. The outcome of these empirical studies indicated a positive increase in usability measures for our design prototypes, including a significant improvement in task times and user satisfaction.
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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 | 0.059 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".