A Study of Cross-Session Cross-Device Search Within an Academic Digital Library
Bibliographic record
Abstract
Information seeking in an academic digital library is complex in nature, often spanning multiple search sessions. Resuming academic search tasks requires significant cognitive effort as searchers must re-acquaint themselves with previous search session activities and previously discovered documents before resuming their search. Further, some academic searchers may find it convenient to initiate such searches on their mobile devices during short gaps in time (e.g., between classes), and resume them later in a desktop environment when they can use the extra screen space and more convenient document storage capabilities of their computers. To support such searching, we have developed an academic digital library search interface that assists searchers in managing cross-session search tasks even when moving between mobile and desktop environments. Using a controlled laboratory study we compared our approach (Dilex) to a standard academic digital library search interface. We found increased user engagement in both the initial (mobile) and resumed (desktop) search activities, and that participants spent more time on the search results pages and had an increased degree of interaction with information and personalization features during the resumed tasks. These results provide evidence that the participants were able to make effective use of the visualization features in Dilex, which enabled them to readily resume their search tasks and stay engaged in the search activities. This work represents an example of how semi-automatic search task/session management and visualization features can support cross-session search, and how designing for both mobile and desktop use can support cross-device search.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".