MétaCan
Menu
Back to cohort
Record W3009991094 · doi:10.5206/tips.v9i1.10320

Effective Classroom Techniques for Engaging Students in Role-Playing

2020· article· en· W3009991094 on OpenAlexaffvenue
Anthony Piscitelli

Bibliographic record

VenueTeaching Innovation Projects · 2020
Typearticle
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsConestoga College
Fundersnot available
KeywordsSession (web analytics)Scripting languageReading (process)PsychologyRole playingStudent engagementPedagogyTeaching methodMathematics educationComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

Role-playing is a teaching technique that provides students with an opportunity to engage with the material in a unique way within the classroom setting. A classroom role-play can involve students reading pre-designed scripts, students play acting characters described on role cards, or students acting out characters of their own creation. Regardless of the specific approach, role-play activities can serve to increase student retention, understanding, and engagement with the course material. In this session, educators explore the benefits and challenges associated with using role-play activities in the classroom. Participants get a chance to experience a role-play activity and consider how to facilitate a role-play that creates a memorable experience and contributes to course learning outcomes. The ultimate goal is to provide participants with the tools to use role-playing in their own teaching practices.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.003

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 head0.083
GPT teacher head0.438
Teacher spread0.355 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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

Citations9
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueTeaching Innovation ProjectsSame topicDigital Storytelling and EducationFrench-language works237,207