Jose-Garcia, A., Sneyd, A., Merlo, A. et al. (6 more authors) (2023) C3-IoC: A career guidance system for assessing student skills using machine learning and network visualisation. International Journal of Artificial Intelligence in Education, 33 (4). pp. 1092-1119. ISSN 1560-4292
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2022. Open Access: This 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/. |
Keywords: | Career guidance system; IT sector; technical and non-technical skills; job network visualisation; text mining; machine learning |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 26 Oct 2022 14:05 |
Last Modified: | 18 Dec 2023 14:54 |
Status: | Published |
Publisher: | Springer |
Refereed: | Yes |
Identification Number: | 10.1007/s40593-022-00317-y |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:192143 |