Using the SQ6R Technique to Enhance Reading Comprehension Abilities for Thai 8th-Graders
Bibliographic record
Abstract
The purposes of the study were to examine the effectiveness of the SQ6R learning management plan on grade 8 students’ reading comprehension, to compare the participants’ reading comprehension before and after the implementation of the learning management plan, and to examine the participants’ satisfaction with the SQ6R learning management plan. 40 Grade 8 students from Phadungnaree School, Maha Sarakham, Thailand were selected through cluster sampling as the samples of the study. The research employed three key instruments: Learning management plan; pre-and post-test measuring students’ reading comprehension ability, and a questionnaire assessing students’ perceptions of the learning experience. The findings revealed that the SQ6R-based learning management plan was effective in developing participants’ reading comprehension. Additionally, students reported high satisfaction, attributing their engagement to the structured, interactive, and student-centered approach of SQ6R. The study adds empirical evidence supporting SQ6R as an effective instructional method for enhancing reading comprehension, reinforcing its applicability in educational policy and classroom practice.
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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) | 0.000 | 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) | 0.005 | 0.001 |
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".