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Simulated-Person Methodology Workshop: Enhancing Pedagogical Practices within Higher Education

2017· article· en· W2997667195 on OpenAlexaffabout
Eva Peisachovich, Samantha Johnson, Iris Epstein, Celina Da Silva, Raya Gal, Lora Appel, Celia Popovic

Bibliographic record

VenueInternational Journal for Cross-Disciplinary Subjects in Education · 2017
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsYork University
Fundersnot available
KeywordsMathematics educationPsychologyPedagogySociologyComputer scienceMedical educationMedicine

Abstract

fetched live from OpenAlex

between professionals is deemed a contributing factor to adverse events. This underscores the need to provide educators with the tools and education to embed methods in their teaching that will enable learners to effectively intervene in highly charged interpersonal situations and high-risk scenarios; these concerns highlight the value of realistic simulated-experiential approaches, such as the one proposed in this project. The first phase of this project involved an experiential workshop, developed and conducted at a Canadian university; the workshop was designed to provide educators with knowledge and skills to work with and effectively utilize simulators, enhancing pedagogical classroom practices for teaching undergraduate learners. The workshop provided educators with opportunities for meaningful reflection on their teaching practice and the ability to apply this insight to optimize student learning. It provided theatre students, recruited as simulators as part of this interdisciplinary initiative, to expand their experiences and this will lead to an expanded practicum course for their program. This paper reflects on the workshop experiences and feedback obtained to provide an understanding of the participants' experiences.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.398
GPT teacher head0.603
Teacher spread0.206 · 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.

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

Citations0
Published2017
Admission routes2
Has abstractyes

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