Effects of Example-Problem Pairs on Students’ Mathematics Achievements: A Mixed-Method Study
Bibliographic record
Abstract
The aim of this research is to investigate how the utilisation of example-problem pairs affects the outcomes of mathematics students when compared to conventional teaching methods. Thus, a mixed method embedded design, with a main emphasis on a quasi-experiment with supplemental field notes, was conducted with 64 second intermediate grade school students (eighth grade). Participants were divided into two groups comprising 33 students in the experimental group, and 31 students in the control group. An ACNOVA test revealed that the average scores of achievement of the students taught using the example-problem pairs were higher than the average scores of the students who were taught using conventional teaching methods, with a very large effect size. Moreover, the qualitative findings revealed that the students taught using example-problem pairs were more engaged and took more responsibility for their learning than the students who were taught using conventional teaching methods. In addition, the students who lacked the necessary prerequisite knowledge needed more support than the higher achieving students. The implications of the study were discussed.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".