A learning community for teens on a virtual island - The Schome Park Teen Second Life Pilot project
Bibliographic record
Abstract
Virtual 3D worlds such as Second Life 2 and online gaming environments are attracting educationalists' interest.This paper reports upon the first European Teen Second Life educational project for 13-17 year olds: the Schome Park NAGTY (National Association for Gifted and Talented Youth) Pilot.This project aimed to collect evidence about fresh approaches to education beyond the existing curricula of formal schooling through exploring the educational potentials and pitfalls of Second Life.Diverse quantitative and qualitative data sources are drawn upon to investigate issues relating to engagement, development of domain-specific and knowledge age skills as well as challenges for educators.Engagement data showed that only approximately one quarter of students accounted for almost all time spent in Schome Park.Frequency was associated with high levels of use of the wiki and forum.Evidence from self-reports and documentation on the wiki demonstrated very high levels of Second Life skills.Knowledge age skills were assessed within a framework with four levels for four dimensions.In respect of Communication, all students who engaged achieved the first level and a substantial minority initiated and moderated discussions and/or organised events.In respect of Teamwork, tensions were evident early on; however, a substantial number demonstrated their abilities to operate at the highest level being actively involved in solving governance problems.With support students moved from hierarchical approaches to the formation of governance groups, each with department officers, thus furnishing evidence of distributed Leadership at level one.Evidence from a rich and diverse programme of events illustrates an atmosphere which fostered Creativity, permitting explorations, collaborations and the encouragement to risk mistakes.Our experience suggests the importance of understanding the role of teachers in this kind of innovative environment, not as the possessors of relevant knowledge but as facilitators and promoters of a cooperative ethos.We conclude that, despite multiple challenges, there is evidence to support dramatic new possibilities for pedagogic redesigns.Students who engaged with the virtual island, the wiki and the forum demonstrated higher levels of the knowledge age skills of communication, leadership, teamwork and creativity.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".