On the Relationship of Multiple Intelligences With Listening Proficiency and Attitudes Among Iranian TEFL University Students
Bibliographic record
Abstract
Gardner’s (1983) Multiple Intelligences Theory (MIT) has been found to have profound implications in teaching English as a foreign language (TEFL) in that it provides a way for teachers to recognize learners’ individual cognitive and affective differences by providing favorable motivational conditions for learning. However, little investigation has focused on the domains of cognition and affect in a single study. Therefore, this study investigates two facets: the relationship of Multiple Intelligences (MIs) with listening among Iranian TEFL university students and the possible relationship between the type of intelligence the students fall into and their attitudes toward learning English. In this study, McKenzie’s (1999) MI Inventory was used to identify 60 participants’ preferred intelligences. The participants comprised an intact group randomly assigned to the experiment. A Likert-type questionnaire was employed to elicit data about participants’ levels of personality traits that accounted for their attitudes to language-learning. Also, the participants’ listening comprehension proficiency was measured using the listening section of a retired TOEFL test. Data analysis using Pearson correlation revealed no significant relationship between the score of listening and any of the MIs. Similarly, the results indicated no significant difference between MIs and attitudes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".