Grammaring, Its Effects on Oral Performance Among EFL Beginner-Level Learners in Higher Education
Bibliographic record
Abstract
Over several decades, numerous approaches applied to EFL have resulted in theories and reasonings to teach and learn English. Although Communicative Language Teaching (CLT) is the most commonly used path nowadays, it has only resulted in minimal development of university learners’ oral skills; i.e., English-beginner-level students usually attain minimal scores on oral performance after instruction using CLT approaches in some Higher Education Institutions. Thus, this study aims to illustrate the impact of Grammaring approach, in combination with the practice of Form and Meaning as a complement to Use in CLT, on students’ oral proficiency. Data from 38 students in control (n=19) and experimental (n=19) groups were analyzed. A descriptive and inferential statistical analysis of rubric bands from pre and post tests showed subtle improvements in aspects of Form (syntax) and Meaning (lexical use) but not in Use. These results have implications on what to teach and how to teach some language skills to lower-level learners, and highlights considerations for elaborating rubrics and assessing foreign language learners.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".