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Record W2979285823 · doi:10.46743/2160-3715/2019.4125

Avatar Kinect: Drama in the Virtual Classroom among L2 Learners of English

2019· article· en· W2979285823 on OpenAlexaff
Robert Bianchi, Byrad Yyelland, Joseph Yang, Molly McHarg

Bibliographic record

VenueThe Qualitative Report · 2019
Typearticle
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsCollege of the North Atlantic
Fundersnot available
KeywordsAvatarPsychologyQualitative researchGestureIdentity (music)EntertainmentPerceptionInterpersonal communicationExperiential learningComputer-mediated communicationSocial psychologyPedagogySociologyThe InternetComputer scienceWorld Wide WebVisual artsHuman–computer interaction

Abstract

fetched live from OpenAlex

This study presents a qualitative approach to exploring classroom behaviour using dramaturgical analysis of student interactions in relation with, and as mediated through, a gesture-based gaming software among L2 learners of English at two international branch campuses in the Arabian Gulf where face-to-face interactions between unrelated members of the opposite sex are generally discouraged. We investigated whether Avatar Kinect might provide a safe way for young males and females to interact while discussing social issues in a composition course. Data were collected through personal observation and survey. Five key themes emerged from the study. First, some participants chose to perform at front stage and others chose to remain back stage. Second, front stage participants chose avatars with gender and skin colour similar to themselves. Third, all participants appeared to be engaged in the interactive role play processes and with one another. Fourth, front stage actors appeared to act without inhibition. Finally, all participants expressed frustration with technology shortcomings.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.309
Threshold uncertainty score0.269

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
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 science0.0000.000
Research integrity0.0000.000
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 head0.079
GPT teacher head0.444
Teacher spread0.365 · 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 designQualitative
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

Citations10
Published2019
Admission routes1
Has abstractyes

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