Making sense of group interaction in an ambient intelligent environment for physical play
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents the results of a study on group interaction with a prototype known as socio-ec(h)o. socio-ec(h)o explores the design of sensing and display, user modeling, and interaction in an embedded interaction system utilizing a game structure. Our study involved the playing of our prototype system by thirty-six (36) participants grouped into teams of four (4). Our aim was to determine heuristics that we could use to further design the interaction and user model approaches for group and embodied interaction systems. We analyzed group interaction and performance based on factors of team cohesion and goal focus. We found that with our system, these factors alone could not explain performance. However, when transitions in the degrees of each factor, i.e. high, medium or low are considered, a clearer picture for performance emerges. The significance of the results is that they describe recognizable factors for positive group interaction. Author Keywords Groups, responsive environment, play, embodiment,
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 it