A Machine Learning Approach to Career Path Choice for Information Technology Graduates

Authors

  • H. Al-Dossari Information Systems Department, King Saud University, Saudi Arabia http://orcid.org/0000-0002-9064-0022
  • F. A. Nughaymish Information Systems Department, King Saud University, Saudi Arabia
  • Z. Al-Qahtani Information Systems Department, King Saud University, Saudi Arabia
  • M. Alkahlifah Information Systems Department, King Saud University, Saudi Arabia
  • A. Alqahtani Information Systems Department, King Saud University, Saudi Arabia
Volume: 10 | Issue: 6 | Pages: 6589-6596 | December 2020 | https://doi.org/10.48084/etasr.3821

Abstract

Enterprises rely more and more on well-qualified and highly specialized IT professionals. Although the increasing availability of IT jobs is a good indicator for IT graduates, they nonetheless may find themselves confused about the most appropriate career for their future. In this paper, a recommendation system called CareerRec is proposed, which uses machine learning algorithms to help IT graduates select a career path based on their skills. CareerRec was trained and tested using a dataset of 2255 employees in the IT sector in Saudi Arabia. We conducted a performance comparison between five machine learning algorithms to assess their accuracy for predicting the best-suited career path among 3 classes. Our experiments demonstrate that the XGBoost algorithm outperforms other models and gives the highest accuracy (70.47%).

Keywords:

recommendation system, information technology graduates, machine learning, career selection

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How to Cite

[1]
H. Al-Dossari, F. A. Nughaymish, Z. Al-Qahtani, M. Alkahlifah, and A. Alqahtani, “A Machine Learning Approach to Career Path Choice for Information Technology Graduates ”, Eng. Technol. Appl. Sci. Res., vol. 10, no. 6, pp. 6589–6596, Dec. 2020.

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