TY - JOUR
T1 - C3-IoC
T2 - A Career Guidance System for Assessing Student Skills using Machine Learning and Network Visualisation
AU - José-García, Adán
AU - Sneyd, Alison
AU - Melro, Ana
AU - Ollagnier, Anaïs
AU - Tarling, Georgina
AU - Zhang, Haiyang
AU - Stevenson, Mark
AU - Everson, Richard
AU - Arthur, Rudy
N1 - Funding Information:
This work was supported by the Institute of Coding in the UK, which received funding from the Office for Students (OfS).
Publisher Copyright:
© 2022, The Author(s).
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Career guidance system
KW - IT sector
KW - Job network visualisation
KW - Machine learning
KW - Technical and non-technical skills
KW - Text mining
UR - http://www.scopus.com/inward/record.url?scp=85143280527&partnerID=8YFLogxK
U2 - 10.1007/s40593-022-00317-y
DO - 10.1007/s40593-022-00317-y
M3 - Article
AN - SCOPUS:85143280527
SN - 1560-4292
VL - 33
SP - 1092
EP - 1119
JO - International Journal of Artificial Intelligence in Education
JF - International Journal of Artificial Intelligence in Education
IS - 4
ER -