With the proliferation of disruptive technologies driving innovation, there arises an imperative for businesses to equip themselves with forward-looking skill sets. The stewards of higher education institutions (HEIs), who oversee the preparation of candidates with pertinent skills, bear a significant responsibility. The workforce is grappling with an expanding skill gap (SG) brought about by the rapid adoption of technology and the evolving landscape of job roles. Thus, academia finds itself presented with a pressing challenge—to bridge this growing divide. To address this concern, we propose the HEISG (higher education institution skill gap) framework, leveraging machine learning (ML) techniques to assess and quantify this SG. This framework calculates skill scores pertaining to both supply and demand by analyzing exit skill scores from HEIs and entry skill scores required by industries, respectively. By comparing the resulting student profiles from HEIs with job descriptions from industry, we compute a cosine similarity score that highlights the gap between the supply and demand skill vectors. This iterative process is aimed at narrowing the chasm between the skills demanded by the industry and those imparted by HEIs. Our proposal suggests updating curricula to align with industry needs. The application of ML algorithms and natural language processing aids in generating skill index (SI) with customizable threshold values, tailored to specific disciplines and sectors. This study contributes to a better understanding of the demand for various skill sets, ultimately ensuring that students are well-prepared for the industry, thanks to the implementation of this framework. © 2025 selection and editorial matter, Urmila Shrawankar and Prerna Mishra; individual chapters, the contributors.
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