Investigating Engineering Graduates Employability Using Advanced Machine Learning: Aggregate Trends and Determinants

Engineering is a very sought-after degree program with an overarching perception that the graduate will most likely be able to secure a suitable job while those from less vocational disciplines may have to struggle more. While youth unemployment is overall on the rise, engineering graduates are increasingly vulnerable.  Ironically, the employers struggle in hiring a suitable candidate whereas unemployment, in particular youth unemployment is on the rise. 

While multiple psychological, economic and social factors are at play, quality and relevance of engineering education imparted at Higher Education Institutions (HEIs) can be a crucial contributor. This research will focus on 5 key stakeholders: academia (faculty and administration), students (current and alumni), employer (industry), parents/families and civil society to ascertain the determinants of engineering youth unemployment and its impact. A qualitative as well as quantitative research will be conducted, and data will be collected using thoughtfully developed instruments. This will be followed by deploying artificial intelligence-based machine learning algorithms for determining the most contributing factors of engineering youth unemployment, predicting unemployment and identifying hidden trends. The analysis will result in recommendations for the educational policy makers and will contribute towards mitigating youth unemployment.

Aqsa Shabbir
Associate Professor Electrical Engineering and Director Research, Innovation and Commercialization
Lahore College for Women University, Lahore
06 months
Rs. 32,00,000/-
Huma Tauseef, Ali Hussain Kazim