The Effects of Integrated Brain-Based Learning and Skills Training in Linear and Quadratic Functions Among Grade 11 Students
Bibliographic record
Abstract
The purposes of the study were 1) to investigate the effectiveness of integrated brain-based learning and skills training on grade 11 students’ learning achievement in linear and quadratic functions 2) to compare the achievement of grade 11 students before and after using integrated brain-based learning and skills training, and 3) to study students’ satisfaction with the integrated brain-based learning and skills training. The participants were 40 grade 11 students in a Thai public school selected by the cluster sampling method. Research instruments were 1) a learning management plan 2) skills training 3) a learning Achievement Test, and 4) a Satisfaction Questionnaire. Statistics used in data analysis were percentage, average, standard deviation, and paired samples t-test. The results of the study indicate that 1) the effectiveness of the integrated brain-based learning and skills training on grade 11 students’ learning achievement in linear and quadratic functions, 2) the achievement on grade 11 students after learning with the learning management was significantly higher than before using the treatmen, and 3) students was satisfied with the learning processes during the implementation of the learning management plan. The result of the study contributes to the area of mathematics education as it presents an alternative instructional method that combines the benefits of teaching principles to teach a complicated concept in mathematics. Moreover, it illustrates how the two principles are integrated to form a learning management plan that could drive learners’ learning process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".