The Development of Reading Comprehension Ability Using the SQ6R Learning Management Approach Combined with Artificial Intelligence (AI) for Grade 8 Students
Bibliographic record
Abstract
This study aimed (a) to examine the effectiveness of the SQ6R–AI learning management plan according to the 80/80 criterion, (b) to determine its effectiveness index, and (c) to compare students’ reading comprehension ability before and after the intervention. The participants were 40 Grade 8 students from Satri Si Suksa School, Roi Et Province, Thailand, selected through cluster random sampling. The research instruments included six SQ6R–AI lesson plans and a 30-item multiple-choice reading comprehension test. Data were analyzed using descriptive statistics, the E1/E2 criterion, the effectiveness index (E.I.), and a paired-samples t-test. Results showed that the learning management plan achieved 94.55/81.50 effectiveness, an effectiveness index of 0.6459, and a statistically significant improvement in posttest scores compared to pretest scores (p < .05). This study contributes a practical and technology-enhanced instructional model that combines structured reading strategies with AI support to improve students’ reading comprehension outcomes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".