Mediation Effect of Environment and Infrastructure on English Language Learning in the Saudi Students: A Structural Equation Modelling Approach
Bibliographic record
Abstract
The study aims to determine the mediation effect (total, direct, and indirect effect) of the environment and infrastructure on the English language learning (ELL) of students in higher education in Saudi Arabia. A self-administered web-based structured questionnaire was employed for the collection. A cross-sectional descriptive research design was used to form the foundation of the research study. A test re-test and an inter-rater test were performed to check the inter-class correlation coefficient between the responses of two-time intervals and two experts. The pilot survey was conducted with a small sample of students. Cronbach's alpha and the Kaiser-Meyer-Olkin (KMO) values were utilized in order to examine the reliability and validity of the results. In addition to this, the discriminant validity of the Average Variance Extracted (AVE) and Composite Reliability (CR) measures was examined.The sample size of the study was 407, and a random sampling was used during the study. The Structural Equation Modeling (SEM) technique was used for mediation analysis for the evaluation of total, direct, and indirect effects. The study's findings concluded that the English language infrastructure (INF) has a greater mediating impact on the learning of the English language (ELL) than that of the environment for the English language (ENR). The SPSS-AMOS 23.0 program was used for all types of statistical calculations. Despite the above meaningful findings, the current study suffered from poor real-time data collection from the Saudi students. This study paves the way for moderation or group analysis between the environment, infrastructure, and English language learning among higher education students in Saudi Arabia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".