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Record W3028726210 · doi:10.5539/ies.v13n6p96

Analyzing the Content of Reading Texts Questions in Flying High for Saudi Arabia

2020· article· en· W3028726210 on OpenAlexvenueno aff
Majed Barashid

Bibliographic record

VenueInternational Education Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Assessment and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyReading (process)ChecklistCognitionContent analysisCategorizationMathematics educationPedagogySocial scienceSociologyLinguistics

Abstract

fetched live from OpenAlex

This study aimed at analyzing the cognitive levels of the wh-questions following the reading texts in the Students’ Book of Flying High for Saudi Arabia One. A Checklist based on the revised cognitive domain of Bloom’s Taxonomy was the instrument used to categorize the cognitive levels of these questions and to determine to what extent they are included properly in the Student’s Book of Flying High for Saudi Arabia One. The main findings revealed that the wh-questions following the reading texts which are included in the textbook under analysis were not properly included. Also, the authors of the textbook overemphasized the lower thinking skills and neglected higher thinking skills. Such study is recommended to shed light upon the role of the other series of Flying High for Saudi Arabia in developing cognitive skills among secondary Saudi students.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.519
Threshold uncertainty score0.265

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.267
GPT teacher head0.506
Teacher spread0.239 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations2
Published2020
Admission routes1
Has abstractyes

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