Effects of Multiple Intelligences Project on University Students’ Motivation towards English Language Learning: A Case Study in Vietnam
Bibliographic record
Abstract
This study examines the impact of applying the Multiple Intelligences Project on university students’ English learning motivation. A quantitative research method was used in the research through multiple regression analysis. A total of 458 students from different universities in Vietnam participated in the study. The research results show that all of the four intelligences selected in the research model, including (1) Intrapersonal intelligence; (2) Bodily-Kinesthetic intelligence; (3) Musical intelligence; and (4) Naturalistic intelligence had a positive impact on students' motivation towards learning English, of which naturalistic intelligence had the greatest impact. On that basis, the study gives some suggestions for teaching and learning English with a view to improving students’ learning motivation. The findings of the study contribute to both theoretical and practical aspects since they proved the importance of Multiple Intelligences theory as an educational theoretical framework, as well as the significance of its application in the teaching process. The results of the research will also form the basis for further research.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".