Predicting the Attitudes and Self-esteem of the Grade 9th Lower Secondary School Students Towards Mathematics from their Perceptions of the Classroom Learning Environment
Bibliographic record
Abstract
This study reports the validity of the hypothesis that students’ perceptions of the learning environment of mathematics classroom may predict their attitudes and self-esteem towards mathematics. It examines data from 487 grade 9 th students from 14 mathematics classes in 7 Vietnamese lower secondary schools to identify how students’ perceptions of the learning environment variables, and the extent to which these predict the attitudes and self-esteem. The results obtained from correlation and multiple regression analyses indicated that if students were satisfied with mathematics learning, and if they found their mathematics class as cohesive, then their self-esteem and attitudes towards mathematics would be positive. In contrast, if students perceived mathematics as difficult, and if they perceived the learning atmosphere as competitive, then their self-esteem and attitudes towards mathematics would be negative. The findings show that a positive learning environment should be created to promote the positive attitudes and self-esteem of students in learning.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".