Identifying College Students’ Multiple Intelligences to Enhance Motivation and Language Proficiency
Why this work is in the frame
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Bibliographic record
Abstract
<p>While most research studies on the theory of multiple intelligences focused on the application of the multiple intelligences domains as separate components, this quasi-experimental research targeted the effect of multiple intelligences as integrated abilities for teaching and learning English at higher education. The purpose of this study was to examine the impact of students’ multiple intelligences profiles on their motivation and language proficiency. The quantitative data was collected from the students of the College of Languages and Translation at Al-Imam Mohammad Ibn Saud Islamic University in Saudi Arabia. The researchers prepared a Likert scale questionnaire to identify students’ multiple intelligences. The participants formed two groups from male and female students who studied English courses at level 3. The first group studied English in a traditional classroom where they relied on memorizing grammatical rules while the second group studied English after identifying their multiple intelligences profiles. Using the Statistical Package for the Social Sciences software (SPSS), data analysis results indicated that ineffective teaching strategies that depended on encouraging learners memorizing language rules hindered students from boosting their language proficiency. The analysis of the data also showed that when students became aware of their multiple intelligences profiles, they managed to enhance their motivation, which helped them improve their language skills. The recommendations of the current research provide creative ideas for using multiple intelligences at higher education, including a model for integrating multiple intelligences for teaching English. The current research is also a contribution in teaching English to college students since it is among only a few studies that have applied Gardner’s theory at higher education.</p>
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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 | 0.001 | 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) | 0.001 | 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