MétaCan
Menu
Back to cohort
Record W4224271292 · doi:10.5539/elt.v15n5p43

The Effect of Inquiry-Based Learning Strategy on Developing Saudi Students’ Meta-Cognitive Reading Comprehension Skills

2022· article· en· W4224271292 on OpenAlexvenueno aff
Reem Fuhaid Alshammari

Bibliographic record

VenueEnglish Language Teaching · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsnot available
FundersShaqra University
KeywordsReading comprehensionPsychologyMathematics educationReading (process)CognitionComprehensionMeta-analysisComputer scienceMedicineLinguistics

Abstract

fetched live from OpenAlex

The main aim of the present study is to develop first year university students' meta-cognitive reading comprehension using inquiry-based learning strategy. Subjects of the study were 106 (53 males, 53 females) first year university students enrolled in two classrooms at colleges representing urban and sub-urban areas in Shaqra University. The present study adopted a quasi-experimental design with one-group. The results of present study showed that first year university students should have the following reading comprehension skills: guessing, description, analyzing and identifying the main idea. Accordingly, the suggested strategy was designed and used to develop these identified meta-cognitive reading comprehension skills. A reading comprehension test, based on the identified skills, was designed and used as a pretest and posttest. Findings of the present study showed that there were statistical differences between the students’ mean score on the pretest and posttest in favor of the posttest, and there were statistical differences between the students’ mean score on the posttest at the level of .05. This can be attributed to the effectiveness of using the inquiry-based learning strategy in developing students' meta-cognitive reading comprehension skills at the university stage.

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)0.006
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.029
GPT teacher head0.371
Teacher spread0.343 · 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 designQualitative
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

Citations6
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueEnglish Language TeachingSame topicEducation and Critical Thinking DevelopmentFrench-language works237,207