Effect of the Problem-Solving Approach on Academic Achievement of Students in Mathematics
Bibliographic record
Abstract
The primary focus of this quantitative experimental study was to explore the effect of the problem-solving approach on the academic achievement of students in mathematics at grade IX and to compare the achievement of boys and girls of grade IX taught using this method at Latinath Secondary School Naugad 03, Darchula. This study was based on experimental research method under the quantitative research design. A sample of 60 students was chosen using purposive stratified random sampling, forming equivalent experimental and control groups of 30 students each. Data were collected using two achievement tests (pre-test and post-test) and structured interview tools. Statistical calculations included mean, standard deviation, two-tailed t-test, and coefficient of variation (CV). The post-test results indicated a significant difference in mean achievement between the groups (mean difference of 4.44), significant at the 0.05level. The coefficient of variation of the achievement of students in the control group (18.14%) was higher than that of the experimental group (12.20%). It was concluded that the experimental group, taught with the problem-solving approach, had better achievement than the control group, which was taught using the traditional approach. Furthermore, while the mean achievement score of boys (30.08) was slightly greater than that of girls (29.68) in the post-test, the difference was not statistically significant at the0.05 level. Thus, the study concluded that the problem-solving approach minimizes the gender difference in learning 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".