Assessing Student Employability to Help Recruiters Find the Right Candidates

Saksham Agrawal (saksham.agrawal@gmail.com)

A chapter in Advances in Analytics and Applications, 2019, pp 161-174 from Springer

Abstract: Abstract India produces lakhs of engineers every year, but employability has been a concern for educators and recruiters alike. Cost of technical recruitment has been driven extremely high due to inconsistent quality of talent available. Objective assessment of students has helped, but recruiters still use simple cutoffs followed by subjective methods like group discussions and personal interviews which are inherently unscalable and expensive. In the present work, we looked at most recent employment data for almost 50,000 fresh engineering graduates and established precise objective relationships between students’ scores across several dimensions, and the students’ employability as measured by actual job offers made to them by employers. Through regression modeling, we were able to develop a composite score that identified employable candidates with five times greater precision than any individual score. We then went a step further to cluster employers on the basis of their ability to choose the stars from among a large pool of candidates, to identify employers who can be advised to further optimize their recruitment spend.

Keywords: Engineering; Education; Employability; Regression modeling; Clustering (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-981-13-1208-3_14

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DOI: 10.1007/978-981-13-1208-3_14

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