Challenges in strengthening the Communication and Behavioral skills for greater employability of engineering students: A study of NCR Delhi (India)
Bibliographic record
Abstract
Engineering education in the last few decades, has been growng incredibly in importance and reputation due to certain skills, technical knowledge and proficiencies that are imparted to the passed out graduates for their employability. It is an accepted fact that India has an immense potential to be a global technology leader. Since independence, the initial focus of government policy was to develop good engineering institutes and provide engineers required for developing the economy. The setting up of the Indian Institutes of Technology, the Regional Engineering Colleges (and their subsequent conversion to the National Institutes of Technology),were targeted at achieving this objective. Indian engineers established their reputation for advanced engineering and design skills. Engineering in India isalso a preferred option for bright students at the 10+2 level. This has resulted in a spurt in the students intake of engineering colleges primarily in the private sector. Despite this, industry leaders whine about the absence of quality engineers for their industry. Also accompanied by major unemployment rates amongst graduating engineers.
 Curiouslyenough, graduates are ready for taking up their jobs, but according to the industry, who will providethememployment for their skills, knowledge and the composite professional values that they bring with them, and in fact they are not considered‘job-ready’ at all.The lowemployabilityis on account of several factors, out of which poor communication and behavioural skills are identified to be two of the mostcritical skill attributes. An in-depthanalysis has beencarried out by developing two detailed structured questionnaires administered on 55 HR Heads from the industry to confirm and ascertain the industry‘sperspectivesabout these skills and on 20 TPOs/Directorsto find out engineering colleges‘ understanding about these skills includingthe challenges and barriersthey face in this process and also to identifypossible solutions.The results clearly indicate mis-match between industry's and institutes perspective on soft skills.The important finding of the study is to develop conducive environment and follow practical/ activities based approach to impart soft skills to engineering graduates
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".