The Effect of Peer Teaching on Mathematics Academic Achievement of the Undergraduate Students in Oman
Bibliographic record
Abstract
This study explored the effects of peer teaching on mathematics academic achievement of the undergraduate students in Oman. The sample of this study composed of (32) undergraduate female students enrolled in the course, “Mathematics for Social Sciences I” in Mathematics and Sciences Unit in Dhofar University in spring semester 2014-2015. The sample of the study was divided randomly into two groups, the first group was selected as experimental group (16) students and the other group was selected as control group (16) students. Both groups studied the same curriculum, same materials, and both groups had been taught by the same instructor. Data were collected by using, Mathematics Achievement Test (MACT) which was prepared by the researchers. In order to analyze data, researchers used mean, standard deviations, and Analysis of t-test. The result of the study found that peer teaching strategy is an active tool to increase the mathematical achievement. The study recommended the instructors and college professors to use peer teaching in order to improve the performance of the students 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".