Brain Based Learning and Its Relation with Multiple Intelligences
Bibliographic record
Abstract
This study aims at exploring the learning that is attributed to the brain and its relationship with multiple intelligences. In order to achieve the goals of the study, two examinations are used. The First one is the examination of the thinking and learning method that is based on both hemispheres of the brain. The second one is the examination of the multiple intelligences. Some referees are consulted for assuring the suitability of the examinations for the measured sample and the calculation of the exam. The sample consists of 300 students who study the course of psychology. The sample is chosen randomly. The results indicates that more repeated method of learning and thinking is based on the left hemisphere of the brain; as it comes out with the highest total of 136 and within a percentage of (45.3%). In addition, the results that are related to the dominance of the multiple intelligences indicate that personal intelligence, and physical intelligence are the highest respectively; a mean value of (49. 80%). Whereas, intrapersonal intelligence comes third with a mean value of (48, 40%). Finally, musical intelligence scores the lowest mean value.Regarding connection relation; it is as a statistical function on the level of the (?=0.05) between the natural intelligence and the left hemisphere of the brain on one hand; and the intrapersonal and the integrated intelligence on the other on the other hand.The study also shows that there is an equal relation with a function at the statistical function of (?=0.01) between the musical intelligence with the right hemisphere and the logical intelligence with the left hemisphere. It is also clear that there is an equal relation between both of (the bodily and the linguistic intelligences) with the left hemisphere and the spatial intelligence with the right hemisphere.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".