The Relationship between Iranian EFL Students' Knowledge of Farsi Grammar and their English Grammar Knowledge
Bibliographic record
Abstract
This study specifically intended to investigate the correlation between Farsi grammar knowledge and English grammar knowledge in relation to students' English language proficiency, age, gender, and linguistic intelligence. The study was conducted with 16 male and 19 female English literature undergraduate students studying at the Foreign Languages Department of state University of Qom, Iran. In this study, one questionnaire and three different tests were administered: a 20-item linguistic intelligence questionnaire in Likert scale model based on Gardner's Multiple Intelligence Theory, a 50-item Nelson Proficiency Test by which the participants were divided into two groups; „high‟ and „low‟ proficient, a 22item multiple-choice test of Farsi grammar knowledge, and a 22-item multiple-choice test of English grammar knowledge. After scoring, some Pearson and Spearman Correlation Coefficient tests were run through SPSS software to investigate the relationship between Farsi and English grammar knowledge, and the relationships between grammar knowledge and English language proficiency, age, and linguistic intelligence. Also, an Independent-Samples T-Test was run to investigate sex differences in terms of L1/L2 grammar knowledge. Finally, it was concluded that: 1. Generally, without considering any other variables, Farsi grammar knowledge and English grammar knowledge are strongly correlated with each other. 2. English language proficiency is correlated with neither English nor Farsi grammar knowledge in none of the groups, i.e. low and high proficient. 3. Linguistic intelligence is significantly correlated with both English and Farsi grammar knowledge. 4. Participants' age is not significantly correlated with either English or Farsi grammar knowledge. 5. Males and females are not significantly different with each other in either English or Farsi grammar knowledge.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".