Investigating the Role of Multiple Intelligences in Determining Vocabulary Learning Strategies for L2 Learners
Bibliographic record
Abstract
<p>This study, first, examined whether there was any relationship between Iranian L2 learners’ vocabulary learning strategies (VLSs), on the one hand, and their multiple intelligences (MI) types, on the other hand. In so doing, it explored the extent to which MI would predict L2 learners’ VLSs. To these ends, 40 L2 learners from Isfahan University of Technology in Isfahan participated in the study, and the following instruments were utilized to collect the data: the Oxford Placement Test (OPT) to gauge participants’ proficiency level, the Multiple Intelligences Questionnaire (Mckenzie, 1999), and a vocabulary learning questionnaire based on the framework adopted from Schmitt’s (1990). The strategies were divided into five categories: determination, memory, social, metacognitive, and cognitive. To analyze the data, Pearson correlation was applied to find out the relationship between the participants’ intelligence categories and their preferred VLSs. Then, multiple regression analysis was run to indicate the significance of the specific VLSs in the participants’ intelligences. Results revealed that there was a strong positive relationship between participants’ intrapersonal intelligence and their tendency toward the cognitive and metacognitive strategies. Moreover, as participants’ scores in their interpersonal intelligence test increased, they inclined toward the social strategies more. A potential positive and significant relationship between visual/spatial intelligence and memory strategies and also linguistic intelligence and determination strategies was also found out. Overall results revealed that the participants made a significant difference regarding their decisions for particular VLSs, as intrapersonal, interpersonal, linguistic, and visual learners predicted more specific and significant VLSs in comparison with other types of intelligences.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".