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Record W1924236807 · doi:10.19173/irrodl.v12i5.923

Role engagement and anonymity in synchronous online role play

2011· article· en· W1924236807 on OpenAlexvenueno aff
Sarah Cornelius, Carole Gordon, Margaret Harris

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsAnonymityContext (archaeology)Perspective (graphical)PsychologyMetaverseComputer-mediated communicationInternet privacyThe InternetComputer scienceWorld Wide WebVirtual realityHuman–computer interaction

Abstract

fetched live from OpenAlex

<p>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.</p><p>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.</p><p> </p>

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.502
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.112
GPT teacher head0.450
Teacher spread0.339 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations25
Published2011
Admission routes1
Has abstractyes

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