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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".