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Record W2902840231 · doi:10.5539/ells.v8n4p8

Impact of Language Input on Comprehensiveness of Reading Material among Students in Saudi Arabia

2018· article· en· W2902840231 on OpenAlexvenueno aff
Mohammed Abdulmalik Ali

Bibliographic record

VenueEnglish Language and Literature Studies · 2018
Typearticle
Languageen
FieldComputer Science
TopicText Readability and Simplification
Canadian institutionsnot available
Fundersnot available
KeywordsVocabularyReading (process)Meaning (existential)ComprehensionReading comprehensionComputer scienceMathematics educationField (mathematics)LinguisticsPsychologyMathematics

Abstract

fetched live from OpenAlex

The aim of the study has been to determine the ways that may facilitate the freshmen at universities, who have English as their second language, with comprehension and understanding of study material. It has included different levels of reading material to the students in order to identify which approach is more convenient for the students to perceive. The approach has concluded that advanced vocabulary and grammatical structure may make it difficult for the students to perceive the meaning of study material. It has been perceived from the study that simplification in the text can bring upon positive impacts on the comprehensibility of content. The comprehensiveness can assist students in learning the study modules yet, it is also presumed that simplification may not enhance the students’ capability to comprehend the second language more efficiently. It is expected that further research in the field may give deeper insight of the linguistic modifications that may improve the comprehending abilities of the 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.010
GPT teacher head0.305
Teacher spread0.295 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2018
Admission routes1
Has abstractyes

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