Promoting Feature Awareness by Leveraging Collaborators' Usage Habits in Collaborative Editors
Bibliographic record
Abstract
Users often rely on their collaborators to find relevant application features by observing them "over the shoulder" (OTS), usually in a synchronous co-located setting. However, as remote work settings have become more common, users can no longer rely on such in-person interaction with collaborators. Therefore, we investigate designs that help the user become aware of relevant features based on collaborators' feature usage habits. We created five design concepts as video prototypes which varied in five design dimensions: number of active collaborators, number of shared documents, specificity of comparison, user involvement, and goal of the feature awareness. Interviews (N=18) probing the design concepts indicate that collaborator-based feature awareness would be valuable for discovering novel features and producing a consistent style across the shared document, but some users may feel micromanaged or self-conscious. We conclude by reflecting on and expanding our design space and discussing future design directions supporting remote OTS learning.
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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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| 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".