Exploring EFL Teachers’ Perceptions of Blended Onsite and Online Teaching at Saudi Universities: Benefits and Challenges
Bibliographic record
Abstract
Blended teaching has become increasingly popular since the COVID-19 pandemic because of how it combines online and in-person learning. However, few studies have examined blended teaching in Saudi Arabia, with limited data on how best to incorporate it into that cultural context. To address that gap, this study investigated how English as a foreign language (EFL) Saudi university teachers perceived this approach. The sample consisted of 123 male and female respondents (out of a target population of roughly all 4720 EFL teachers in the country). The participating teachers were typically in their 30s and in the first few years of their teaching career. Quantitative and qualitative data were collected from a structured questionnaire (including open-ended, closed-ended, and Likert-scale questions) to determine the perceived advantages and disadvantages of this approach. The majority of respondents found blended teaching to be advantageous due to its flexibility in terms of time and location. Over half also believed it helped meet the different teaching modality needs of students. The biggest problems reported with this approach were workload and time management. Another issue was lack of technological infrastructure and support for this teaching model. The ANOVA results indicated that the predictors (perceived benefits, perceived challenges, and technical support) were correlated with teachers’ attitudes toward blended teaching, validating the perceived importance of these factors. Such concerns could be alleviated by better technology, training, and guidance on navigating the dual modalities of face-to-face and online learning. Institutions and policymakers are recommended to consider these issues in order to improve the implementation of blended teaching.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".