The Learning Styles and Multiple Intelligences of EFL College Students in Kuwait
Bibliographic record
Abstract
The study aimed to investigate the learning styles and multiple intelligences of English as foreign language (EFL) college-level students. “Convenience sampling” (Patton, 2015) was used to collect data from a population of 250 students enrolled in seven different academic departments at the College of Basic Education in Kuwait. The data elicitation instrument was derived from two standardized surveys: one on learning styles (Oxford, 1998) and one on multiple intelligences (Christison, 1998). Data collection utilized the Google Forms interface to facilitate participants’ access and responses to survey items through their mobile phones. Data analysis identified the participants’ general learning styles and multiple intelligences. The Microsoft Excel software program was used by the researchers to generate means, percentages, ranks, and standard deviations. Results indicated that while the participants’ dominant learning styles were global, extroverted, hands-on, and visual, their dominant multiple intelligences were interpersonal, visual, and kinesthetic. Implications for pedagogy included recommendations to accommodate students’ visual learning styles and multiple intelligences through the use of visual stimuli like PowerPoint presentations, charts, and graphs. In order to accommodate students’ extraverted and hands on learning styles as well as their interpersonal and kinesthetic intelligences, the researchers recommended the use of group activities such as role plays, simulations, and debates. Implications for future research included conducting learning styles and multiple intelligences studies in other colleges in Kuwait.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".