Why Engineering Education is Losing Charm among Students in India? A Discussion
Bibliographic record
Abstract
After a significant expansion in the number of engineering institutions and intake capacity in India for almost a quarter century, i.e., between 1991-2014, a deceleration has been experienced. At a time when technical intensity has increased, even for non-technical sector, and the primacy is to achieve Sustainable Development Goals by 2030 for which technology needs to play a central role, deceleration in enrolment may not be a good sign. In this background, the paper discusses probable reasons like existing high unemployment among engineers, high cost of private engineering education, and inability of institutions to impart employability skills which fade away their chances to get employed in future too, for this falling enrolment. Moreover, medical education has experienced expansion in the recent past. Generally, software, and IT firms impart their own training after recruiting fresh graduates so they tend to recruit engineers of any branch or even science graduates, where the cost of getting a degree is lower than engineering. Use of emerging technologies in business has made the engineering labour market quite dynamic and is changing the old paradigm of workplace, working hours, static employable skills, etc.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".