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C3-IoC: A Career Guidance System for Assessing Student Skills using Machine Learning and Network Visualisation

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 workf...

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Autores principales: José-García, Adán, Sneyd, Alison, Melro, Ana, Ollagnier, Anaïs, Tarling, Georgina, Zhang, Haiyang, Stevenson, Mark, Everson, Richard, Arthur, Rudy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer New York 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9715283/
https://www.ncbi.nlm.nih.gov/pubmed/36474618
http://dx.doi.org/10.1007/s40593-022-00317-y
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author José-García, Adán
Sneyd, Alison
Melro, Ana
Ollagnier, Anaïs
Tarling, Georgina
Zhang, Haiyang
Stevenson, Mark
Everson, Richard
Arthur, Rudy
author_facet José-García, Adán
Sneyd, Alison
Melro, Ana
Ollagnier, Anaïs
Tarling, Georgina
Zhang, Haiyang
Stevenson, Mark
Everson, Richard
Arthur, Rudy
author_sort José-García, Adán
collection PubMed
description 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.
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spelling pubmed-97152832022-12-02 C3-IoC: A Career Guidance System for Assessing Student Skills using Machine Learning and Network Visualisation José-García, Adán Sneyd, Alison Melro, Ana Ollagnier, Anaïs Tarling, Georgina Zhang, Haiyang Stevenson, Mark Everson, Richard Arthur, Rudy Int J Artif Intell Educ Article 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. Springer New York 2022-12-01 /pmc/articles/PMC9715283/ /pubmed/36474618 http://dx.doi.org/10.1007/s40593-022-00317-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
José-García, Adán
Sneyd, Alison
Melro, Ana
Ollagnier, Anaïs
Tarling, Georgina
Zhang, Haiyang
Stevenson, Mark
Everson, Richard
Arthur, Rudy
C3-IoC: A Career Guidance System for Assessing Student Skills using Machine Learning and Network Visualisation
title C3-IoC: A Career Guidance System for Assessing Student Skills using Machine Learning and Network Visualisation
title_full C3-IoC: A Career Guidance System for Assessing Student Skills using Machine Learning and Network Visualisation
title_fullStr C3-IoC: A Career Guidance System for Assessing Student Skills using Machine Learning and Network Visualisation
title_full_unstemmed C3-IoC: A Career Guidance System for Assessing Student Skills using Machine Learning and Network Visualisation
title_short C3-IoC: A Career Guidance System for Assessing Student Skills using Machine Learning and Network Visualisation
title_sort c3-ioc: a career guidance system for assessing student skills using machine learning and network visualisation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9715283/
https://www.ncbi.nlm.nih.gov/pubmed/36474618
http://dx.doi.org/10.1007/s40593-022-00317-y
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