The Learning Styles and Multiple Intelligences of EFL College Students in Kuwait
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it