Fake it 'til you load it: User Perceptions and Performance with Fast-Loading “False Front” Web Pages
Bibliographic record
Abstract
Long page-load time for web applications is a common frustration for users – and despite substantial advances in web infrastructure, pages still regularly take more than a second to load. A potential solution to this problem is to show users a “false front” page that looks like the website (and loads quickly), and then switch to the real page once it has arrived. False front pages quickly show users the visual appearance of the page – but because they have not yet loaded the code for interaction, they may cause problems when users try to click on items or manipulate the interface. To provide a better understanding of how users perceive and perform with false-front pages, we developed a framework that specifies potential architectures and designs for several types of false front pages. Our false-front mechanism shows a realistic-looking version of a web application and allows users to “click ahead” before the full page has loaded (feedback shows users their click has been received, and clicks are queued for execution when the application becomes available). We provide details on how the different architectures can be implemented, and discuss two reference implementations that we have constructed. We carried out two crowdsourced studies that compared our false front pages to traditional representations of loading (an animated spinner and a skeleton screen), to see whether users were confused or frustrated by the false fronts, and to see how they perceived the pages’ loading time and responsiveness. Our results showed that false fronts led to better ratings of responsiveness and speed, faster task completion, and higher preference – suggesting that early loading of false fronts is a promising avenue to improve user experience with web applications.
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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 | 0.004 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".