Exploring the Impact of SQ4R Technique on Reading Comprehension in 10th-Grade Students
Bibliographic record
Abstract
The study was conducted with the primary objective of investigating the effectiveness of the SQ4R technique in enhancing the reading comprehension skills of 10th-grade students. To achieve this objective, a quasi-experimental research design was chosen, involving a single group of participants. The study cohort consisted of 40 10th-grade students from a secondary school in Thailand. The instruments were an SQ4R learning management plan and a specially designed reading comprehension test. Data analysis in this study was conducted using two distinct approaches. First, the effectiveness index (E.I), calculated as the ratio of E1 (process effectiveness) to E2 (product effectiveness), was employed to gauge the overall effectiveness of the intervention. Second, a paired sample test was utilized to determine any statistically significant differences between the participants' pre-test and post-test scores. The data collection process spanned an entire semester and was conducted within a public school in Thailand during the first semester of the 2023 academic year. The results obtained from this study unequivocally demonstrate the positive and constructive impact of the SQ4R technique on the reading comprehension abilities of the participating students. These findings not only contribute valuable insights to the field of education but also emphasize the practical utility of the SQ4R model in enhancing reading comprehension, particularly within the context of 10th-grade students in Thailand.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".