The effects of group dynamics on learning in virtual world 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
The MA Education in Virtual Worlds is an entirely online programme that facilitates the study of virtual world environments as places where learning can \ntake place. The synchronous tutorials, workshops and seminar sessions all take place in the virtual world Second Life and have a particular emphasis on experiential and situated learning. Due to its distance learning format and \naccessible nature, students take part in the programme from countries all over the world. They meet regularly every week in the virtual world, and those meetings take place through the personae of their avatars. The first year of the programme (Sep 2012-May 2013) was a pilot run with 7 students taking part from the UK, New Zealand and Greece. This first year run was characterised by rapid gelling as \na group and enthusiasm, but they also displayed insecurity, both in relation to the online environment if they were “newbies”, or in relation to the level of study if \nMasters level study was new to them. They demonstrated little or no sense of competition between cohort members. Their assessment outcomes were excellent \nand they demonstrated much creativity in their approach to learning. The current run (Sep 2013-May 2014) has a larger recruitment of 20 students, resident in the UK, Argentina, USA, Canada, Germany and Saudi Arabia. Early indicators of the current first year group are that the levels of enthusiasm are very similar, but some of the early adjectives that characterise this cohort include insecure, committed,curious and competitive. This paper discusses the ongoing findings of an observational and evaluative study of the nature of the group dynamics amongst cohorts on the programme, and the effects these dynamics may have upon learning. \nKey Words: Group dynamics, learning, forming, storming, norming, performing.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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