A Machine Learning Approach to Career Path Choice for Information Technology Graduates
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 selectionDownloads
References
C. M. Veneri, Here Today, Jobs of Tomorrow: Opportunities in Information Technology. Bureau of Labor Statistics, 1998.
J. Bobadilla, F. Ortega, A. Hernando, and A. Gutiérrez, "Recommender systems survey," Knowledge-Based Systems, vol. 46, pp. 109-132, Jul. 2013. DOI: https://doi.org/10.1016/j.knosys.2013.03.012
L. Csorny, "Careers in the Growing Field of Information Technology Services," Beyond the Numbers, vol. 2, no. 9, Apr. 2013.
L. Stewart, "Looking for Jobs? Look to IT in 2012 and Beyond," Information Technology and Innovation Foundation, 2012.
D. Richards and D. Terkanian, "Occupational employment projections to 2022," Monthly Labor Review, U.S. Bureau of Labor Statistics, Dec. 2013. DOI: https://doi.org/10.21916/mlr.2013.41
S. K. White, "The 10 most in-demand tech jobs for 2020 - and how to hire for them," CIO, Jan. 10, 2020. https://www.cio.com/article/3235944/hiring-the-most-in-demand-tech-jobs-for-2018.html (accessed Dec. 12, 2020).
ICT Report: ICT Workforce in the Kingdom of Saudi Arabia. Riyadh, Saudi Arabia: CITC Publications, 2015.
H. Ma, I. King, and M. R. Lyu, "Effective missing data prediction for collaborative filtering," in Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, New York, NY, USA, Jul. 2007, pp. 39-46. DOI: https://doi.org/10.1145/1277741.1277751
P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, "GroupLens: an open architecture for collaborative filtering of netnews," in Proceedings of the 1994 ACM conference on Computer supported cooperative work, New York, NY, USA, Oct. 1994, pp. 175-186. DOI: https://doi.org/10.1145/192844.192905
J. S. Breese, D. Heckerman, and C. Kadie, "Empirical analysis of predictive algorithms for collaborative filtering," in Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, San Francisco, CA, USA, Jul. 1998, pp. 43-52.
B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, "Item-based collaborative filtering recommendation algorithms," in Proceedings of the 10th international conference on World Wide Web, New York, NY, USA, Apr. 2001, pp. 285-295. DOI: https://doi.org/10.1145/371920.372071
M. C. B. Natividad, B. D. Gerardo, and R. P. Medina, "A fuzzy-based career recommender system for senior high school students in K to 12 education," IOP Conference Series: Materials Science and Engineering, vol. 482, Mar. 2019, Art. no. 012025. DOI: https://doi.org/10.1088/1757-899X/482/1/012025
A. O. Ogunde and J. O. Idialu, "A recommender system for selecting potential industrial training organizations," Engineering Reports, vol. 1, no. 3, 2019, Art. no. e12046. DOI: https://doi.org/10.1002/eng2.12046
M. Kiran, H. Asim, and M. T. Hassan, "Career and Skills Recommendations using Data Mining Technique: Matching Right People for Right Profession, in Pakistani Context," VFAST Transactions on Software Engineering, vol. 7, no. 1, pp. 33-41-41, Dec. 2018.
D. Grewal and K. Kaur, "Developing an Intelligent Recommendation System for Course Selection by Students for Graduate Courses," Business and Economics Journal, vol. 7, no. 2, 2015, Art. no. 1000209. DOI: https://doi.org/10.4172/2151-6219.1000209
M. A. Al-Barrak and M. Al-Razgan, "Predicting Students Final GPA Using Decision Trees: A Case Study," International Journal of Information and Education Technology, vol. 6, no. 7, pp. 528-533, 2016. DOI: https://doi.org/10.7763/IJIET.2016.V6.745
A. Alhassan, B. Zafar, and A. Mueen, "Predict Students' Academic Performance based on their Assessment Grades and Online Activity Data," International Journal of Advanced Computer Science and Applications (IJACSA), vol. 11, no. 4, Jun. 2020. DOI: https://doi.org/10.14569/IJACSA.2020.0110425
P. Shayan and E. Iscioglu, "An Assessment of Students' Satisfaction Level from Learning Management Systems: Case Study of Payamnoor and Farhangian Universities," Engineering, Technology & Applied Science Research, vol. 7, no. 4, pp. 1874-1878, Aug. 2017. DOI: https://doi.org/10.48084/etasr.1041
G. C. Keshvari, J. M. Faraz, N. Safaie, and S. J. H. Nedjad, "A Model For the Development οf Employees' Learning (Career Path) in Industrial Enterprises," Engineering, Technology & Applied Science Research, vol. 8, no. 1, pp. 2427-2432, Feb. 2018. DOI: https://doi.org/10.48084/etasr.1687
H. Al-Ballaa, H. Al-Dossari, and A. Chikh, "Using an Exponential Random Graph Model to Recommend Academic Collaborators," Information, vol. 10, no. 6, p. 220, Jun. 2019. DOI: https://doi.org/10.3390/info10060220
M. Sahami, S. Roach, E. Cuadros-Vargas, and R. LeBlanc, "ACM/IEEE-CS computer science curriculum 2013: reviewing the ironman report," in Proceeding of the 44th ACM technical symposium on Computer science education, New York, NY, USA, Mar. 2013, pp. 13-14. DOI: https://doi.org/10.1145/2445196.2445206
International Standard Classification of Occupations 2008 (ISCO-08): Structure, group definitions and correspondence tables. Geneva, Switzerland: ILO, 2012.
The Unified Saudi Occupational Classifiation. Riyadh, Saudi Arabia: General Authority for Statistics, 2019.
D. W. Aha, D. Kibler, and M. K. Albert, "Instance-based learning algorithms," Machine Learning, vol. 6, no. 1, pp. 37-66, Jan. 1991. DOI: https://doi.org/10.1007/BF00153759
J. R. Quinlan, C4.5: Programs for Machine Learning, 1st ed. San Mateo, CA, USA: Morgan Kaufmann, 1992.
L. Breiman, "Bagging predictors," Machine Learning, vol. 24, no. 2, pp. 123-140, Aug. 1996. DOI: https://doi.org/10.1007/BF00058655
J. H. Friedman, "Stochastic gradient boosting," Computational Statistics & Data Analysis, vol. 38, no. 4, pp. 367-378, Feb. 2002. DOI: https://doi.org/10.1016/S0167-9473(01)00065-2
T. Chen and T. He, xgboost: eXtreme Gradient Boosting. 2020.
Downloads
How to Cite
License
Copyright (c) 2020 Authors
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
