The Effectiveness of Peer Tutoring in Enhancing Reading Comprehension of Ninth Grade Students
Bibliographic record
Abstract
The study entitled The Effectiveness of Peer Tutoring in Enhancing Reading Comprehension of Grade 9 Students aims to determine the effectiveness of using peer tutoring as a strategy or tool to enhance the reading comprehension of the Grade 9 Students of Samal National High School.The research study utilized an experimental research specifically using pretest-posttest design to investigate the topic.The primary data were collected from a sample of sixty (60) students using a researcher-made reading comprehension questionnaire that had been validated by a panel of experts.Statistical tools such as weighted mean, and t-test were applied to analyze and interpret the data.The results indicated that the ninth grade students achieved the required level of reading comprehension.The study revealed a significant difference in reading comprehension before and after the implementation of peer tutoring, supporting the notion that peer tutoring contributed to the improvement of reading comprehension.As a result, it is recommended to conduct similar studies with a broader understanding of strategies for enhancing reading comprehension, beyond the focus on peer tutoring that has been demonstrated to be effective.Future researchers should encompass a wider scope, different research locations, and additional factors that were not considered in the present study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".