The Relationship between Multiple Intelligences and Writing Ability of Iranian EFL Learners
Bibliographic record
Abstract
The relationship between multiple intelligences and learning of L2 language skills is a burgeoning area of research. This study aimed at finding the relationship between Multiple Intelligences (MI) and the writing ability of EFL learners, For this purpose, the body of female BA sophomores in TEFL at Urmia University (N = 47), within the age range of 18-25, was given a close look using an intact group research design. The proposed hypothesis predicted no significant relationship between MI and writing ability of the participants. The participants were given Armstrong's MI questionnaire which used a Likert Scale. The participants' writing samples were also obtained using an IELTS writing task and were correlated with the scores on the MI questionnaire. The scoring of writing was done analytically following pre-specified criteria. The writings were scored by two raters yielding an inter-rater reliability of 0.8. Results obtained through Multiple Regression indicated that the components of MI did not have a significant relationship with the writing ability of the participants. Detailed results and implications are discussed in the paper.
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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 | 0.004 |
| 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".