Attitudes of Students of Medicine Toward Oral Presentations as Part of Their ESP (English for Specific Purposes) Course
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".