The effects of group dynamics on learning in virtual world environments
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 cantake 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 andaccessible 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 asa 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 ifMasters level study was new to them. They demonstrated little or no sense of competition between cohort members. Their assessment outcomes were excellentand 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.Key Words: Group dynamics, learning, forming, storming, norming, performing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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; a candidate call from one source (direct Gemma or distilled Codex), 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".