Measuring the Effect of Cognitive and Metacognitive Questioning Strategies on EFL Learners’ Reading Comprehension in Understanding, Critical Thinking and the Quality of Schema at the University of Hail-KSA
Bibliographic record
Abstract
It was found out that the cognitive and the metacognitive strategies which enable students to use their prior knowledge or schemata increase the level of students’ engagement in the learning process and thus stimulate their critical thinking and a greater awareness of other perspectives in reading. Therefore, this study which is based on the main principles of the schema theory aims at training EFL learners at the University of Hail on making connections between their prior knowledge and the reading text to improve students’ understanding, critical thinking, and the quality of schema. Five questioning strategies were incorporated to make training more effective: KWL, questioning the author, self-questioning, guided questions, and making connections strategy such as self-to text connections, text-to text connections and text to world connections. The sample of the study consists of two groups: experimental and control. A schema-based test was designed to measure the students’ achievement before and after the experiment. The results then were analyzed by t-test. It was found out that the type of instruction that students receive affects reading comprehension. And thus, teaching students to use cognitive and metacognitive strategies enables effective reading comprehension.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".