EFL Students’ Perceptions of a Blended Learning Environment: Advantages, Limitations and Suggestions for Improvement
Bibliographic record
Abstract
This study explores King Khalid University English as Foreign Language (EFL) students' views regarding the advantages and limitations of merging the features of face-to-face language instruction and online language learning via the Blackboard learning management system in a new pedagogical approach called Blended Learning. The study also examines students' suggestions for improving the quality of Blended Learning courses. The sample consists of 160 male students. The participants completed a 33-item questionnaire. The results indicate the clear advantages of this new experience in broadening students' reading opportunities and enriching their English vocabulary. Moreover, the component on advantages clearly demonstrates how Blended Learning provides an environment for more effective employment of indirect language learning strategies (Oxford, 1990) such as meta-cognitive strategies (arranging and planning learning), affective strategies (confidence enhancement), and social strategies (cooperating with others).The limitations and problems of Blended Learning highlighted by the respondents are followed by a number of practical suggestions for addressing these drawbacks, including solving technical problems, providing proper training to students, increasing the number of labs, and recognizing excellent performance of both instructors and students. Translating these suggestions into an action plan and a road map will enhance the effectiveness of using Blended Learning to create supportive learning opportunities for language learners.
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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 | 0.008 | 0.013 |
| 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 | 0.004 | 0.003 |
| Open science | 0.001 | 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".