The Effectiveness of an Educational Program Built on the Brain-Based Learning Theory in Improving Mathematical Skills and Motivation for Learning among Student with Learning Disabilities in Jordan
Bibliographic record
Abstract
This study aimed at investigating the effectiveness of an educational program built on the brain-based learning theory in improving the mathematical skills and motivation among students with learning disabilities. The sample of the study consisted of (60) student enrolled in learning disabilities’ recourses rooms from the third, fourth and fifth grades. The sample was divided randomly into two groups; an experimental group and a control group. In order to achieve the objectives of the study, the researchers have developed three achievement tests in Math for the third, fourth and fifth grades, mathematic motivation scale, and the psychometric properties of the scale in order to apply the pre-post-tests. The researchers also designed the educational program base on the brain-based learning theory. The implementation of the program took two consecutive months; (75) lessons, (2) lessons per day with a duration of (45) minutes for each lesson. After obtaining the results through the appropriate statistical analysis, the study concluded that there were statistically significant differences in the post-test of mathematical skills and its sub-dimensions in favour of the experimental group. There was no statistically significant effect for both gender and grade variables and the interaction between the educational program and grade on the achievement of mathematics skills. There were statistically significant differences on the post-test of motivation to learn mathematics and its sub-dimensions and in favour of the experimental group.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".