A Gender-Based Study to Investigate Saudi Male and Female EFL Learners’ Satisfaction Towards the Effectiveness of Hybrid Learning
Bibliographic record
Abstract
Since the transmission of knowledge has started, it solely relied on traditional teaching methods but ever since technology-mediated instructions have emerged, they potentially brought a revolution in how we teach, when we teach, from where we teach and what gadgets, modes and apps can better cater learners’ interest and motivation. In this context, hybrid learning is a novel approach in academic settings that embraces advantage of the retention of face-to-face component of traditional classes and e-learning environment. The present study aims at investigating Taif University’s male and female English as Foreign Language (EFL) learners’ satisfaction towards the onsite and online learning environments. An opinionnaire with 20 items was developed with closed ended questions by employing Likert’s five-point scale to collect the data from 200 male and female EFL participants of Taif University, represents quantitative dimensions of the study. The research tool is designed to measure learners’ satisfaction that is further categorized into five subscales. These include: (a) learners’ satisfaction with the instructor and their real-time feedback; (b) perceived ease of use of technology and internet; (c) effective course content and interactive and collaborative activities; (d) finally engaging nature of hybrid learning and its impacts on learners’ interest and motivation. The study finds no significant differences in male and female participants’ perceptions regarding effective delivery of hybrid instructions except meek variations in male and female learners’ preferences in perceived ease of use of technology. The statistics reveal that male participants and their female counterparts slightly differ in their satisfaction level towards the technical problems faced by them in recording their scores, flexibility in terms of time and space, and in smooth completion of online activities. Finally, the study provides few recommendations to fix certain issues and improve the quality of hybrid learning environment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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.
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".