Impact of Consciousness-Raising Activities on Young English Language Learners’ Grammar Performance
Bibliographic record
Abstract
Grammar Consciousness-Raising (GCR) is an approach to teaching of grammar which learners instead of being taught the given rules, experience language data. The data challenge them to rethink, restructure their existing mental grammar and construct an explicit rule to describe the grammatical feature which the data illustrate (Ellis, 2002). And also GCR approach encourages learners to pay attention to language form (Richards & Smidth, 2002). The present study aimed to consider the effectiveness of grammar consciousness-raising activities on the development of young EFL learners’ grammar performance and also to study the appropriateness of consciousness-raising for young learners. The participants were 60 young Iranian male and female pre-intermediate students with the age range of 11 to 16. The participants were randomly assigned to two groups. The Experimental group was exposed to grammar consciousness-raising activities based on three techniques of Recall, Reconstruction, and Bolding-Underlying while the control group was trained by deductive grammar teaching. A grammar test was administered to the participants before and after the treatment as pretest and posttest. And also by applying a questionnaire to the experimental group, the learners showed their attitude toward consciousness-raising. The results revealed that grammar consciousness-raising activities have a significant effect on the development of young learners’ grammar performance and consciousness-raising is suitable for young 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".