Analysis the Educational Factor of Graduate Students from a Bachelor’s Degree in English as a Foreign Language
Bibliographic record
Abstract
This article seeks to raise awareness about the impact of graduate students from an undergraduate English program at a public university, from 2011 to 2015, in the geographic scope field. Once they have completed their educational process, which emphasizes on English as a foreign language as well as on the pedagogical and research fields.The main purpose of this paper is to analyze the impact of graduate students from an English teaching program to determine the possible weakness or strengthens that let us consider some modifications in the curriculum that currently educates future teachers.To achieve this purpose, it was necessary to establish a study of these future teachers. As a consequence, an analysis was carried out on the data collected by using interviews, applied to the population.This study showed the high level of employability achieved by the students from the Bachelor’s degree in English, once they finished their studies. It was also observed, that there was a low occupancy in the research field, regardless of being one of the emphases of the curriculum offered by this academic program.Despite this achievement, it is necessary to ask about the perception of employers on the graduates and their employability, as well as the effectiveness of their learning process and their level of performance in the educational field, as a global teacher.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".