Value Role of ICT Tools in English Language Teaching and Learning- Emphasis on Covid-19 Pandemic
Bibliographic record
Abstract
Communicative knowledge and digital knowledge have become essential components of our everyday lives. English language is one of the major global means of communication. Information and communication technology (ICT) plays an important role in many areas of life, including education. The contemporary immense waves of globalization and the increased dominance of the English language over the political, cultural, and economic levels necessitate our effective preparation for the young generation to acquire the abilities and skills that help them meet the needs of their future careers. The ability to speak English effectively and to handle various ICT Tools purposefully has become an essential need for the young generation to cope with the current information revolution. The aim of this study is to evaluate the function of ICT technologies, in English language instruction and acquisition with a focus on the COVID-19 pandemic. This study focuses on ICT resources including systems, Internet, mobile apps, websites, language learning centers, and YouTube, how ICT tools were used during the COVID-19 pandemic and how they helped teachers and students in the classroom. The study concludes that each instrument described plays an important role in English educational activities, as well as in increasing the learner’s skills, helps learners work at their own pace and gives learners more control over their own learning, and facilities collaborative and cooperative learning.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".