Bibliographic record
Abstract
The applications of multiple intelligence theory in education are wide. Students apply the learning in the classroom according to their own dominant intelligence and learning style, which is most effective for them. Combining learning styles with dominant intelligences enhances the students' learning processes.The purpose of this case study is to examine the relationship between dominant intelligences according to Gardner's multiple intelligence theory and middle school students' academic achievement. A case study was conducted in Israel, in a middle school, among seventh-graders and involved 158 students.Findings indicated that in excellent classes - 80.9% of students had logical intelligence, in at least one of the levels of dominance; in ordinary classes only 48.4% of students have logical intelligence, at least in one of the levels of dominance. We also examined the relationship between the amount of dominant intelligences among students in all classes, excellent and ordinary. Findings indicated that in excellent classes the percentage of students with two or three dominant intelligences was higher than the percentage in ordinary classes. It is important to note that these are not just the logical and verbal, but also all types of intelligences, such as spatial, musical, kinetic and others.In conclusion, the dominant intelligences that highly influence and measure achievement in the education system are not the logical-mathematical and the linguistic-verbal, but the only logical-mathematical. Moreover, the amount of intelligences at the dominant levels can predict and indicate student's success at school.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".