The effects of spatial layout on relationships between performance, path patterns and mental representation in a hypermedia information search task
Bibliographic record
Abstract
The purpose of this study is to build on previous research in hypermedia by including an investigation of the relationships between navigation tools, path patterns and mental representations with traditional measures of navigation outcomes. We examined the effects of four different spatial layouts on three facets of hypermedia use, performance, path patterns and mental representation, during an information search task. Typically, such measures are evaluated independently. We have sought to reveal what types of information in a navigation tool might mediate links between these three aspects of hypermedia use. The performance measures indicated that providing certain types of spatial information does not enhance speed, accuracy or economy but does enhance recall of page titles. Reference is then made to an earlier analysis on the dataset of path patterns using Multidimensional Scaling (MDS) which indicated that users’ paths reflected the most prominent type of information provided in the navigation tool. The MDS configurations were then compared to the results of a distance‐like ratings task using correlation and regression methods. Only users given explicit spatial cues in the navigation tool exhibited ratings that reflected the paths they had actually taken. Although spatial information may not impact surface performance measures such as speed and economy, spatial information does play a role in influencing where users go and the development of their mental representations of the material in a hyper document.
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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 | 0.002 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".