Language Proficiency Level and Intake of Nominal Group Use in Scientific English: A Web Classroom Empirical Study
Bibliographic record
Abstract
Focusing on the teaching-learning of nominal group use in science and technology through input noticing, this article deals with a pedagogical application of technological resources to the acquisition of grammar competence in English by a group of 50 senior year Spanish engineering students with different CEFR English level, ranging from A2 to C1. Students received training to notice nominal groups in an input of specialized engineering articles in the Web classroom, as part of the core subject English for Academic and Professional Communication syllabus, taught at the Technical University of Madrid (Spain). The results of the statistical analysis revealed that the entire group improved their ability to use noun compounds correctly, and that the students’ CEFR level affects the student’s initial and final marks, but not their improvement in nominal group use. It was concluded that this approach, which integrated technology-enhanced noticing into the course methodology, improved the students’ performance and, therefore, can be profitably implemented for the acquisition of grammar competence.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".