The Relationship Between Multiple Intelligences and Language Learning Strategies and Gender
Bibliographic record
Abstract
This study set out with the aim of assessing whether Multiple Intelligences profiles of Iranian students would exert any influence on their use of language learning strategies as important determining factors in the language learning. Additionally, we explored the role of gender and different proficiency levels on EFL learners’ multiple intelligences. A total number of 303 EFL learners, 164 males and 139 females participated in this study, 112 were Elementary, 92 were Intermediate students and 99 were advanced level, within the age range of 12 to 33 at Jahade Daneshgahi of Tabriz. The instruments used to elicit information for this study were MIDAS and the Strategy Inventory for Language Learning (SILL) Questionnaire. Initially, we homogenized the English proficiency of the participants, by administering Nelson English Language Tests. Results showed a significant relationship between the variables of multiple intelligences (MI) and Strategy Inventory of Language Learning (SIL). Results of multivariate tests showed a significant positive difference between the MI scores and different proficiency levels but no significant difference in MI scores across genders. a significant difference was found in musical intelligence of participants at different proficiency levels. In terms of implications of the study it is suggested that before choosing any teaching materials, educators should conduct needs analysis and test in order to find out the MI profile of the students and to avoid having any mismatch between selected topics and the students’ needs.
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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".