A Comparison of Learning Styles and Study Strategies Used by Low and High Math Achieving Brunei Secondary School Students: Implications for Teaching
Bibliographic record
Abstract
The survey assessed the learning styles and study strategies used by 135 randomly selected Brunei secondary school students and compared them by educational level, math ability, and gender. Junior students (Forms 1-3) rely heavily on the use of the written-expressive learning style than their senior counterparts (Forms 4-5). In addition, the more able math students dominantly use the auditory-language learning style than their less able peers. Furthermore, high math achievers were better and more efficient users of the text book reading, note-taking, and memory study strategies than low achievers. Moreover, female students were more effective and superior users of the visual-language and auditory-visual-kinesthetic learning styles including the text book reading, note-taking, memory, test preparation, and concentration study strategies. These are perhaps some of the reasons why females perform better at math than males. Overall, the findings seem to have wide-ranging implications for teaching students with high support needs in mathematics.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".