Professional Development of English Professors in Indian Engineering Colleges: The Need of the Hour
Bibliographic record
Abstract
English has become the language of international business and in this age of globalization, communication skills in the English language are of supreme importance in the professional success of individuals. In India, the percentage of engineering graduates who remain unemployed after graduation steadily increases due to lack of soft skills including the ability to communicate in English. Hence, the major responsibility of enhancing the students’ communication skills falls on the shoulders of the English professors. This article aims to find out whether English professors in engineering colleges in Chennai, India are equipped to train engineering students to communicate efficiently by using modern methods of teaching. It has been discovered that many English professors are not aware of the modern teaching methodologies like CLT and haven’t heard of English for Specific Purposes. Moreover, many teachers have not attended any pre-service or in-service training programs and there is a huge gap between classroom teaching practices and industry expectations. The survey results have revealed the lack of skills among the professors and the need for professional development programs to improve the efficiency of English teaching.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".