Investigation of Interactive Strategies Used in Undertaking Collaborative Tasks
Bibliographic record
Abstract
Collaborative virtual environments (CVEs) present ways for human users to work together on a task. Research efforts of CVEs have mainly focused on the factors that affect the task performance of the users, such as awareness, communication, and presence. Furthermore, the users involved normally have the same task experience or knowledge background. In this paper, we examined the effect of interactive strategies for human/human interaction, which involves human users with different levels of expertise. Within a CVE for an expert and a novice, we deployed three interactive strategies—Tele-Operation, Tele-Assistance3, and Tele-Assistance7—to give the three degrees of autonomy to the novice. Focusing on the performance of the novice, our findings revealed that each interactive strategy has a different effect on the task performance of the novice. These findings could aid in choosing an optimal interactive strategy for performing a collaborative task between an expert and a novice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".