Factors that make an impact on the English proficiency of students in Intermediate and advanced English II courses of the foreign Language Department of the University of El Salvador during semester I /2017
Bibliographic record
Abstract
A Globalized world demands for a communication channel through which better opportunities are going to be available for ones who fit in its profile, the language that got on this position is English and in consequence the English proficiency, the ability in language use (Bachman, 1990) plays an important role for students who pretend to get in a better life style in this new order. Some factors are going to make a remarkable influence during the teaching learning process for students to get English as foreign language, the main one is motivation; Gardner (1985) motivation is seen as ‘referring to the extent to which the individual works or strives to learn the language because of a desire to do so and the satisfaction experienced in this activity. In similar way, Brown (1980) opines that attitude is the way that you think and feel about something; these together with other variable factors may affect or significantly contribute to language learning students´ process. Language-related extra-curricular activities in universities are an excellent tool to motivate language learners and help them by providing an additional milieu for language practice. Learners in Canada and Russia report a positive impact of ECAs on all the language skills, on building confidence, developing speaking and communication skills. The learners also find that ECA participation helps to overcome shyness and nervousness (Apples – Journal of Applied Language Studies Vol. 11, 1, 2017, 59).
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".