The Study of the Metalinguistic Knowledge of English by Students in an Intensive and a Traditional Course
Bibliographic record
Abstract
The goal of the current research was to study the relationship among learning contexts and, levels of metalinguistic knowledge of the Iranian intermediate EFL learners. This research explores the level of learners’metalinguistic knowledge in English in two different contexts (traditional and intensive courses). Participants included 44 intermediate students at Shoukoh Language Institute, Zanjan, Iran. The selection violated the randomization criterion, thus the quasi-experimental was taken for the current study. The instruments used for data collection were Nelson English Language Proficiency Test (NELPT) as a placement test which used to measure level of students prior to the experiment and, a metalinguistic knowledge English test (MKET) was also, administered at the beginning and ending the semester as pre and post-test to measure their metalinguistic knowledge. The data collected from the administration of the above mentioned two tests were submitted to different statistical analysis such as ANCOVA, one independent sample t-test and, one paired sample t-test. The results revealed that there was a significant distinction between two sets performance in the metalinguistic test. An intensive English course had an important helpful influence on MKE of the students. They enhanced their MKE in an intensive semester. As an implication of this study, the findings will motivate language teachers to focus on intensive semester because intensive instruction was found to be effective in improving the EFL learners’ MKE. Further study is needed before the results of the research can be generalized.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".