The Effects of Blended Learning on First-Year Arab University Students' Oral Production
Bibliographic record
Abstract
Arab language learners struggle with oral production due to limited exposure to the English language, the absence of direct interaction with English-speaking populations, and the prevailing English teaching and learning approaches offered by educational institutions. Blended learning approaches in language teaching and learning were established in the English as a Foreign Language (EFL) context to improve students’ skills. This study examined the impact of a blended learning approach on first-year Arab university male and female students’ oral production using a mixed-methods’ approach. The study enrolled 120 First-Year Arab university students from four Arab countries (Syria, Jordan, Egypt, and Saudi Arabia). Study participants from each country were then randomized into two groups: experimental and control groups. Additionally, four teachers and four students volunteered to participate in study interviews. Data was collected using three research instruments: The Oxford Online Placement Test, the pre-test and post-test, and semi-structured interviews. The use of the blended learning approach in the EFL context had a positive impact on first-year Arab university students' oral production. Using a blended learning approach in EFL contexts can enhance students’ achievement and improve students’ engagement. However, instructors were faced with barriers such as limited technological infrastructure, uneven digital literacy, and cultural norms and values when attempting to use the blended learning approach in Arab EFL classrooms. There are significant implications for instructors and institutions that seek to use a blended learning approach, such as pedagogical adaptation, technological competence, content creation, and individualized learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".