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Record W4415096502 · doi:10.5539/jel.v15n1p342

Students’ Perceptions of English as a Medium of Instruction in Saudi Universities with Respect to Their Intellectual and Linguistic Abilities

2025· article· en· W4415096502 on OpenAlexvenueno aff
Wafa Jeza Alotaibi

Bibliographic record

VenueJournal of Education and Learning · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsMedium of instructionComprehensionRemedial educationPerceptionArabicLanguage proficiencyFace (sociological concept)Reading comprehension

Abstract

fetched live from OpenAlex

English is the primary medium of instruction (EMI) in Saudi universities, especially in science and technical disciplines, where English language proficiency is essential. However, students hold mixed perceptions regarding the use of EMI, with their intellectual and linguistic capabilities playing a significant role. This study surveyed 108 students from four Saudi universities to explore their views on EMI and its effect on their intellectual and linguistic abilities. Results showed that even though 68% of students prefer English as the medium of instruction, 32% face comprehension difficulties, and 58% need to devote additional time due to language barriers. Moreover, 74% reported noticeable improvements in their linguistic abilities. The study also demonstrates challenges such as the necessity for instructors to code-switch between Arabic and English. It suggests short-term strategies such as adopting translanguaging, as well as long-term solutions. A balanced instructional approach is recommended while remedial measures are implemented to enhance English language proficiency.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.307

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.010
GPT teacher head0.270
Teacher spread0.261 · 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 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

Citations1
Published2025
Admission routes1
Has abstractyes

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