Self-concept and Performance of Secondary School Students in Mathematics
Bibliographic record
Abstract
The study investigated the relationship between self-concept and performance in Mathematics as well as theinfluence of gender on self-concept and performance in Mathematics. 320 SS1 students (male=160, female=160)were used for the study. They were selected from 16 secondary schools (urban=8, rural=8) in eight localgovernment areas of Ekiti State. Random sampling was used to select the local government areas, while stratifiedrandom sampling technique was used to select the schools and the participants. Data were collected using a20-item self-concept questionnaire and a 30-item multiple-choice Mathematics Achievement Test with reliabilitycoefficients of 0.74 and 0.83 respectively, and analysed using Pearson product moment correlation and t-teststatistics, tested at 0.05 level of significance. The results showed that self-concept moderately correlated withperformance in Mathematics, while gender had no significant influence on self-concept and performance inMathematics. However, the mean scores of male and female students in Mathematics were below average. It wassuggested that teachers should develop in their students positive self-concept towards Mathematics and pleasantteaching experiences to enhance higher self-concept and better performance 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".