Role engagement and anonymity in synchronous online role play
Bibliographic record
Abstract
Role play activities provide opportunities for learners to adopt unfamiliar roles, engage in interactions with others, and get involved in realistic tasks. They are often recommended to foster the development of soft skills and a wider perspective of the world. Such activities are widely used as an online teaching approach, with examples ranging from the simple use of email to the employment of virtual worlds and Web 2.0 technologies. This paper provides a case study of a role play activity which employs real-time anonymous discussion forums and aims to improve our understanding of effective role play and the impact of anonymity. This role play has been effective in educating learners about different perspectives on the issue of Quality in Further Education. The context and implementation of the role play are outlined, and the learners’ interactions and experiences are explored using an investigative analysis of discussion transcripts and semi-structured interviews with participants. The findings suggest that role engagement and anonymity are important components for success in synchronous online role play. Evidence is presented that provides an insight into the factors which encourage role engagement, including prior experiences and contributions from peers. The impact of anonymity is also explored since many participants did not regard the study environment as real and attempted to identify their peers.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".