Grammar Translation Method and Neurolinguistics Analysis in Level B1 of Higher Education
Bibliographic record
Abstract
This research aims to explain how Ecuadorian students of higher education learn English. The grammar-translation method helps Spanish-speaking students learn English more efficiently. These students try to connect their native language with the second language based on what they want to express, and this connection deals with neurolinguistics. The teacher must provide an efficient explanation so that the students can develop an activity, which is why the grammar-translation method was an important part of the learning process. When the students got the idea of the grammar point, they could develop language skills like reading, listening and producing the language by writing and speaking. Therefore, grammar translation is just the first step in teaching development. This process was applied with Level B1 students of Universidad Nacional de Chimborazo, who could improve their knowledge in using English as a second language and being sure about what they understood, the students could demonstrate what they learned through written and spoken reports. This work was based on qualitative and quantitative method, the analysis of the grammar points presented in level B1 topics and the examples applied both in English and Spanish. It also explains how the students developed the activities and the results they achieved. The real point is what the students think and feel about learning English by translation.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".