Investigating Relationship Development Processes Between 3D Conversational Agents and Learners in Collaborative Discussions
Bibliographic record
Abstract
Social agents (SA) like robots or conversational agents can be great catalysts for fostering co-regulation of collaborative discussions as learning peers who help learners maintain apt participation in group discussions. However, prior CSCL studies have rarely addressed SA's design of sociability, which should be a key for SA to become actual learning peers. This paper describes a qualitative analysis of face-to-face group discussions to investigate the relationship development between learners and a three-dimensional holographic SA. The analysis revealed that the learners' initial impressions of the SA varied between positive and negative; sometimes the learners even ignored or refuted the SA's prompt at first. However, the relationship between the SA and the learners improved as the learners recognized the SA's abilities and potential, so they engaged in the discussion according to the SA's prompt. The authors discussed the need for research in designing SA and its circumstances to achieve effective facilitation.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.014 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".