The Impact of Visual Layout Factors on Performance in Web Pages: A Cross-Language Study
Bibliographic record
Abstract
Visual layout has a strong impact on performance and is a critical factor in the design of graphical user interfaces (GUIs) and Web pages. Many design guidelines employed in Web page design were inherited from human performance literature and GUI design studies and practices. However, few studies have investigated the more specific patterns of performance with Web pages that may reflect some differences between Web page and GUI design. We investigated interactions among four visual layout factors in Web page design (quantity of links, alignment, grouping indications, and density) in two experiments: one with pages in Hebrew, entailing right-to-left reading, and the other with English pages, entailing left-to-right reading. Some performance patterns (measured by search times and eye movements) were similar between languages. Performance was particularly poor in pages with many links and variable densities, but it improved with the presence of uniform density. Alignment was not shown to be a performance-enhancing factor. The findings are discussed in terms of the similarities and differences in the impact of layout factors between GUIs and Web pages. Actual or potential applications of this research include specific guidelines for Web page design.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".