Enhancing the Design of Web Navigation Systems: The Influence of User Disorientation on Engagement and Performance1
Bibliographic record
Abstract
This paper draws on research from a wide literature base to develop a model relating Web navigation systems, disorientation, engagement, user performance, and intentions. The model is tested in an experimental study examining the effects of one simple and two global navigation systems. Although well-accepted design guidelines were followed for the first global navigation system, it was not superior to the simple system. However, the second global navigation system resulted in lower disorientation than the simple system. Based on the study’s results, two design guidelines to govern the development of future Web-based systems are suggested. Readers need a sense of context, of their place within an organization of information. In paper documents this sense of “where you are” is a mixture of graphic and editorial organizational cues supplied by the graphic design of the book, the organization of the text, and the physical sensation of the book as an object. Electronic documents provide none of the physical cues we take for granted in assessing information. When we see a Web hypertext link on the page we have few cues to where we will be led, how much information is at the other end of the link, and exactly how the linked information relates to the current page. Even the view of individual Web pages is restricted for many users. (Lynch and Horton 2002) On IBM’s website, the most popular feature was the search function, because the site was difficult to navigate. The second most popular feature was the “help” button, because the search technology was so ineffective. IBM’s solution was a 10-week effort to redesign the site.…In the first week after the redesign, use of the “help” button decreased 84 percent, while sales increased 400 percent. (UsabilityNet 2003)
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.
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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".