COMMUNITY OF INQUIRY AND LEARNING IN IMMERSIVE ENVIRONMENTS
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 describes an exploratory, observational study using a purposive sample selection to determine if the presence indicators of the well regarded Community of Inquiry model can be a useful tool to observe and assess learning events which use a Multi User Virtual Environment (MUVE) as the mode of delivery [1]. Specific research questions addressed include whether the Community of Inquiry is recognizable in a MUVE learning environment, if new presence indicators are required if observing a MUVE learning event and finally, does the community of inquiry offer a base rubric to determine the educational effectiveness of learning events which take place in a MUVE? The results are promising; while new presence indicators add breadth to understanding the nature of learning in an immersiveenvironment the core construct of the community of inquiry does indeed transfer to this emerging learning technology.
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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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