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Record W2749081980 · doi:10.3968/9533

Attitudes of Students of Medicine Toward Oral Presentations as Part of Their ESP (English for Specific Purposes) Course

2017· article· en· W2749081980 on OpenAlexvenueno aff
Giti Karimkhanlooei

Bibliographic record

VenueHigher education of social science · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsViewpointsPsychologyMedical educationMathematics educationMedicine

Abstract

fetched live from OpenAlex

This article takes a functional approach to examine the attitudes of medical students toward oral presentations in English for specific purposes (ESP) courses by focusing on the performances delivered by the students to catch the precise attitude in respect of the effectivity. The article reflects on the shift of attention from sole teacher-centered classes via helping learners to communicate in the globalized age of knowledge by means of emphasis on oral presentations. This approach considers the students’ viewpoints about implementing oral presentations in ESP classes. The attitudes, meanwhile, adhere to learners’ problems which hinder them to be active participants and presenters in their classes. However, there are controversies about the appropriateness and constraints of oral presentations by students in an ESP and EFL learning environment. It seems that with meticulous structured planning and organization, oral presentations can lead to valuable for both learners and teachers. While, such activities account for a break away from textbooks, but provide for students such a learning setting that they attend classes with preplanning and excitement. The introduction of oral presentations to ESP classrooms encourages learners in training themselves to have confident presentations in public. This fact is especially true for students of medicine who find themselves in need of taking part at international programs where they need to speak out their research findings and science-based academic developments.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.756
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.090
GPT teacher head0.402
Teacher spread0.312 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

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