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Record W2963698880 · doi:10.5539/ijel.v9n5p12

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

2019· article· en· W2963698880 on OpenAlexvenueno aff
Lina Abedelqader Mohmmad Salameh, Zakaria A. Salameh, Aida H. Al-Emami

Bibliographic record

VenueInternational Journal of English Linguistics · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsnot available
FundersUniversity of Hail
KeywordsReading comprehensionMetacognitionSchema (genetic algorithms)PsychologyCognitionMathematics educationComprehensionCritical thinkingReading (process)PedagogyComputer scienceLinguistics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.064
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.752
Threshold uncertainty score0.944

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.064
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.057
GPT teacher head0.361
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2019
Admission routes1
Has abstractyes

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