Analyzing the Employability Skills of Engineering Graduates using AHP Techniques - A Case Study of Kerala State in India.
Bibliographic record
Abstract
In the present era, only competent graduates can survive in the global economy. The Washington accord suggested twelve graduate attributes essential for competent engineering graduates. Various accreditation agencies measure the competency of engineering graduates in terms of these graduate attributes. This paper presented the perception of academicians’ and industry professionals’ regarding the most important skill needed for a competent engineering graduates. Also, the paper discussed how far the present undergraduate engineering curriculum prepares the engineering graduates to be industry ready. Identification of the most important skill needed for engineering graduate is done by employing one of the multi criteria decision method called Analytical hierarchy process (AHP). AHP incorporates several criteria and order of preference in evaluating and selecting the best option among many alternatives based on the desired outcome. The responses from academicians as well as industry professionals from Civil Engineering stream in Kerala, India were collected. The criteria weights were determined based on the procedure given by Saaty. The consistency index values reinforced the reliability of judgment. The study showcased that problem solving skill and teamwork are the most important skill needed for an engineering graduate from academicians’ viewpoint. According to industry professionals, engineering knowledge is more important than problem solving skills. Also, in the present study academicians and industry professionals unanimously suggested the revision of curriculum, internships for students, the collaboration between academicians and industry professionals both in academia and industry, exposure of students to real world problems are some of the means to develop competency in Civil 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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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".