Actually Segregated, Virtually Integrated! The Impact of Virtual Learning on the Sustainable Development of Saudi EFL Students: A Focus on Gender Equality
Bibliographic record
Abstract
This paper is based on the premise that virtual learning is a softener of gender segregation in the Saudi education settings. It explores how virtual learning interconnects with gender equality within the framework of sustainable development. The main objective of the paper is to demonstrate the extent to which virtual learning contributes to softening gender segregation, particularly in a gender-segregated-oriented culture, where gender segregation in education is predominant and virtual learning is a momentous avenue for educational access. The study uses a mixed-method analysis represented by both a quantitative and qualitative analysis. Two instruments are used in data collection: a questionnaire and an interview. The sample of the study consists of 373 female students, who are studying English as a foreign language in four different academic departments in one Saudi university, and 16 male teachers affiliated with the same academic departments in the same Saudi university. Results reveal that virtual learning can bridge gender gaps by providing equal learning opportunities, and, therefore, it can be perceived as a gender-segregation softener. Also, there is a statistically significant correlation between virtual learning and gender equality in terms of female students' academic performance and engagement. This study recommends additional pedagogical applications and uses of virtual learning platforms as a way to reduce gender segregation, particularly in educational settings that are gender-segregated.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".