What Can Searching Behavior Tell Us About the Difficulty of Information Tasks? A Study of Web Navigation
Bibliographic record
Abstract
Abstract Task has been recognized as an influential factor in information seeking behavior. An increasing number of studies are concentrating on the specific characteristics of the task as independent variables to explain associated information‐seeking activities. This paper examines the relationships between operational measures of information search behavior, subjectively perceived post‐task difficulty and objective task complexity in the context of factual information‐seeking tasks on the web. A question‐driven, web‐based information‐finding study was conducted in a controlled experimental setting. The study participants performed nine search tasks of varying complexity. Subjective task difficulty was found to be correlated with many measures that characterize the searcher's activities. Four of those measures, the number of the unique web pages visited, the time spent on each page, the degree of deviation from the optimal path and the degree of the navigation path's linearity, were found to be good predictors of subjective task difficulty. Objective task complexity was found to affect the relative importance of those predictors and to affect subjective assessment of task difficulty.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".