The Effects of Motivational Factors in Learning among Students in Secondary School in Negeri Sembilan
Bibliographic record
Abstract
This article highlights some of the major research findings regarding the extrinsic motivational factors in learning Mathematic among Students in Secondary School in Negeri Sembilan. This study is aimed to find out the relationship between extrinsic motivational factors which include teacher, peer-group, family, environment, and language with the academic performances in Mathematic. This study also aimed to find out the dominant extrinsic motivational factor in learning Mathematic among the factors studied. A total of 203 Form Four students from all three Students in Secondary Schools in Negeri Sembilan were chosen to participate in this study by using random sampling method. The extrinsic motivational factors in learning Mathematic were measured using a self-constructed questionnaire that includes all motivational factors investigated. The internal consistency reliability coefficient of Alpha Cronbach, a for this study was 0.816. Pearson Correlation used to test the relationships among extrinsic motivational factors and academic performances in Mathematic. Results of the study showed all five extrinsic motivational factors have significant relationship with academic achievement of Mathematic. Peer-group has the most significant mean where it is the most significant extrinsic motivational factor in this study. Keyword: Mathematic, achievement, environment, extrinsic motivation, peer-group, language.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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".