Effectiveness of Blended Approach in Teaching and Learning of Language Skills in Saudi Context: A Case Study
Bibliographic record
Abstract
The blended approach serves as an effective interface between web-based and face-to-face teaching and learning of language skills. It offers the best of both and commoditizes broad-based teaching and learning avenues thereby bringing the whole teaching and learning process to life. An empirical study conducted on EFL/ESP teachers and learners of Saudi universities indicates that adopting a fully online or a fully offline approach is not as effective as a blended approach. The overwhelming majority of the respondents illustrate that a blended approach offers a rich variety of alternatives combining both online and offline platforms. It is also evident from the findings of the current study that even a technophobic teacher of the old generation can enrich his pedagogical effectiveness while navigating and integrating a vast variety of authentic online resources in his face-to-face teaching. Nevertheless, a learner can also learn language skills effectively by interacting with the dynamic instructors in a face-to-face environment and by repeatedly using online audio-video learning resources at his convenient time. In fact, modern learners are becoming more tech-savvy owing to an exponential growth of Internet usage during the current pandemic of COVID-19, and hence willing to embrace digital learning to enhance their learning experiences. So, let both get intertwined and go hand in hand to revitalize both teaching and learning activities. The amalgamation of the interactive dynamic environment of offline and individualized/independent learning online offers a rounded learning experience.
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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 | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".