Gender, Need-Achievement and Assertiveness as Factors of Conceptions about Math among Secondary School Students in Ogun State, Nigeria
Bibliographic record
Abstract
This study investigated the influence of Gender, need-achievement and assertiveness on conceptions about math among senior secondary school students in Ijebu-Ode Local government area of Ogun state. Using an ex-post facto survey research design and multiple sampling methods, a total of 350 participants participated in the study. 127 (36.3%) were male, 223 (63.7%) were female. 150 (42.9%) were from female only schools, 150 (42.9%) were from co-educational schools and 50 (14.3%) from male only schools. Validated scale was used for data collection. The two hypotheses stated were rejected based on statistical insignificance. There was no significant relationship among gender, need achievement, assertiveness and conceptions. It thus follows that there are other variables, (apart from the ones that were considered in this study) that significantly influence conceptions about mathematics among secondary school students. Attention should therefore be focused on further studies in this area, in order to be able to pigeon-hole the factors that may account for the different conceptions about math, so that right measures will be applied to help students change the wrong conceptions and be better in mathematics as a school subject.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".