Impact of English Language Teaching and Learning through Language Laboratory in Engineering in Nepal
Bibliographic record
Abstract
The present article discusses the importance of language, in general, and English, in particular, in the context of engineering education in Nepal. It mainly discusses the importance and application of language laboratory for the enhancement of skills and proficiency of English language in the learners of Bachelor's level in engineering in Nepal. The main objective of the present article is to highlight the importance of teaching and learning of English language in the B.E. level in engineering by using language laboratory. In course of the present study, literature available in different accessible sources was reviewed for collecting necessary data and designing theoretical framework for the same. From the study, it has been found that language laboratory is tremendously helpful in creating favourable atmoshphere for language learning and helping the learners to acquire necessary language skills useful to them in sharpening their study at present and streamlining their research and innovative activities in their further studies in the future. English language is now the language of worldwide communication, and therefore, it is very essential for the students of engineering to be proficient in communication through English not only for grabbing job opportunities open at present but also to furthering their future research and innovative endeavors and publishing their reports and research articles based on them. Language Laboratory is highly instrumental for attaining ample proficiency in English language, the means of global communication.DOI: http://dx.doi.org/10.3126/jie.v10i1.10882Journal of the Institute of Engineering, Vol. 10, No. 1, 2014, pp. 94–103
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".