Using Blended Approach for EFL Learning: A Step towards 21st Century Classrooms
Bibliographic record
Abstract
The increasing use of technology for the teaching-learning activity has seen a significant change in the learning approach across the globe including the teaching of English as a foreign/second language. In this context, the teacher makes use of online classes along with the formal or in class approach for EFL learning. Consequently, the blended learning approach has seen an influx of research and considered as a potential area of research for the teachers. As opposed to the sheer use of e-learning, blended learning promotes the use of different technological equipment for EFL instructions in addition to the traditional method or face to face approach. Therefore, several higher learning centers have already started using blended learning to teach EFL learners. However, this phenomenon is more prevalent in the developed nations as compared to the developing countries like Saudi Arabia. Therefore, the author aims to determine the attitudes and perceptions of EFL learners towards the use of blended learning, as an ultimate recipient and recommends it for further implementation based on the findings of this study. The questionnaire has randomly been administered among a total of 70 undergraduate EFL learners of Qassim University, Saudi Arabia. The questionnaire consists of 10 closed ended items. Based on the collected responses of EFL learners against each item a quantitative analysis has been done using SPSS 26. The results indicate that most EFL learners believe that it has a positive impact and make learning more interesting. Further, the study has been concluded with the recommendations and practical implication in EFL learning based on the obtained results.
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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 | 0.002 | 0.003 |
| 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 | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".