Development of Grade 11 student Learning Achievements on Quadratic Functions Using Brain-Based Learning (BBL) Management
Bibliographic record
Abstract
Integrating the knowledge of neurology in teaching has been an idea for learning management design for decades. The current study found the potential of brain-based learning (BBL) for developing high school students’ mathematics learning achievement. The purposes of the study were to investigate the effects of BBL learning management on grade 11 students’ learning achievement of quadratic functions and to examine students’ satisfaction with the BBL as the main principle of learning activities design. The participants were 36 grade 11 students selected by the cluster sampling method. The instruments were brain-based learning management, a learning achievement test, and a satisfaction questionnaire. The statistics used in data analysis were percentage, mean score, standard deviation, a paired t-test, and the index of effectiveness with the determining criteria of 75/75. The findings indicate the benefits of the BBL on mathematics education. They illustrate how learners nearing the end of high school could understand a complex mathematical idea using the BBL instructional strategy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".