C3-IoC: A Career Guidance System for Assessing Student Skills using Machine Learning and Network Visualisation
Adán José-García, Alison Sneyd, Ana Melro, Anaïs Ollagnier, Georgina Tarling, Haiyang Zhang, Mark Stevenson, Richard Everson, Rudy Arthur
Abstract
Artificial Intelligence in Education (AIED) has witnessed significant growth over the last twenty-five years, providing a wide range of technologies to support academic, institutional, and administrative services. More recently, AIED applications have been developed to prepare students for the workforce, providing career guidance services for higher education. However, this remains challenging, especially concerning the rapidly changing labour market in the IT sector. In this paper, we introduce an AI-based solution named C3-IoC (https://c3-ioc.co.uk), which intends to help students explore career paths in IT according to their level of education, skills and prior experience. The C3-IoC presents a novel similarity metric method for relating existing job roles to a range of technical and non-technical skills. This also allows the visualisation of a job role network, placing the student within communities of job roles. Using a unique knowledge base, user skill profiling, job role matching, and visualisation modules, the C3-IoC supports students in self-evaluating their skills and understanding how they relate to emerging IT jobs. Supplementary Information: The online version contains supplementary material available at 10.1007/s40593-022-00317-y.