A Review of the Literature on the Integration of Technology into the Learning and Teaching of English Language Skills
Bibliographic record
Abstract
Technologies have dramatically changed the way people gather information, carry out research, and communicate with others worldwide. Technology has removed the distance obstacles and has made it possible for higher education to effectively teach anyone. Technology integration is being increasingly used in instruction to improve teaching and learning. This rapid development of technology integration has presented a better pattern to find the new teaching models. Consequently, it has a key role in learning and teaching language skills. The integration of technology to create a context to teach and learn English skills has a lot of advantages. The fundamental aim of this paper is to review the issues related to technology integration in the learning and teaching of language skills. In this paper, the researcher defines the term technology integration, expresses the reason of integrating technology, explains the role of technologies in promoting learning, elaborates teachers’ roles and learners’ roles, reviews previous studies on the benefits of technology in the learning and teaching of language skills, indicates the situation of Information and Communication Technologies (ICTs) in Iran, and finally mentions the recommendations for the successful integration of technology. The review of literature revealed that the integration of technology into the classrooms considerably improves the learning and teaching of English language skills.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".