Dynamic Learning Platform for Dynamic EFL Learners: A New Journey to Navigate Effective E-Learning Program for English Education at the University Level
Bibliographic record
Abstract
With the innovation of technology, the nature of learning and teaching English as a Foreign Language (EFL) has been changed, and instead of traditional learning and teaching, e-learning has emerged as a new way to meet the demands of the ‘iGeneration’ who can easily roam around the knowledge domain just with a smartphone at anytime from anywhere. This research aims to find out the challenges of e-language learning that will help establish a more sophisticated Virtual Learning Environment (VLE) by implementing the latest version of the Learning Management System (LMS). E-learning is ‘constructive learning’ or ‘self-regulated learning’ where the learners work as ‘knowledge generators’ and teachers as ‘facilitators’ and so, the learning should be accomplished in a platform that ensures interaction, communication, and collaboration. This research will be provocative of establishing such an environment where online foreign language learners and instructors can adapt easily to the new pedagogical approach to learning for successfully attaining the target learning outcomes. A survey was conducted at the department of English, Najran University (Girls’ section), the Kingdom of Saudi Arabia, and the findings show the necessity of improving teachers’ efficiency in selecting e-learning tools to design learning materials and contents of the course considering cultural context and the modern-day learners’ capacity as well as the goal. It also discusses various implications regarding the need for e-language learners’ skill development that vigorously affects the learning process. The paper finally recommends some possible steps that can be adopted by a university for the successful implementation of e-learning to widen the realm of knowledge for the learners of English as Foreign Language (EFL).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".