Understanding Saudi EFL Learners’ Perspectives on the Efficacy of the Flipped Learning Model: Problems and Prospects
Bibliographic record
Abstract
The study assesses Saudi EFL learners’ perspectives on the efficacy of the flipped learning model, problems and challenges, and opportunities and prospects in Saudi Arabia. 261 respondents from different levels of the undergraduate program at the College of Science and College of Business Administration, Prince Sattam bin Abdulaziz University, Al-Kharj, KSA, were selected using a random sampling method. A questionnaire, with the adoption of modified items from some previous studies, was designed to collect the data. As the research looks at how specific independent variables (gender, year of study, and parents’ professional profile) shape the dependent variable (perspective/perceptions), the data was evaluated quantitatively (utilizing descriptive statistics). The findings revealed that Saudi EFL learners considered flipped classrooms an excellent alternative mode of instruction. In addition, it was observed that this approach to learning encountered certain challenges, such as lack of motivation, learners' incapacity to maintain self-discipline, and unmonitored instruction. It was also revealed that flipped classrooms in Saudi Arabia had considerable prospects due to its young tech-savvy population and its well-advanced infrastructure for learning. One-way ANOVA analysis found no significant statistical difference among the respondents based on their demographic profile. The study implies that issues related to motivation, self-regulated learning, and the learners' failure to stay disciplined in flipped classrooms must be addressed to make it an effective alternative mode of instruction.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".