International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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Utilizing Machine Learning Algorithms for Recruitment Predictions of IT Graduates in the Saudi Labor Market

  • Munirah Alghamlas (Information Technology Department, College of Computer and Information Sciences, King Saud University) ;
  • Reham Alabduljabbar (Information Technology Department, College of Computer and Information Sciences, King Saud University)
  • Received : 2024.03.05
  • Published : 2024.03.30
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Abstract

One of the goals of the Saudi Arabia 2030 vision is to ensure full employment of its citizens. Recruitment of graduates depends on the quality of skills that they may have gained during their study. Hence, the quality of education and ensuring that graduates have sufficient knowledge about the in-demand skills of the market are necessary. However, IT graduates are usually not aware of whether they are suitable for recruitment or not. This study builds a prediction model that can be deployed on the web, where users can input variables to generate predictions. Furthermore, it provides data-driven recommendations of the in-demand skills in the Saudi IT labor market to overcome the unemployment problem. Data were collected from two online job portals: LinkedIn and Bayt.com. Three machine learning algorithms, namely, Support Vector Machine, k-Nearest Neighbor, and Naïve Bayes were used to build the model. Furthermore, descriptive and data analysis methods were employed herein to evaluate the existing gap. Results showed that there existed a gap between labor market employers' expectations of Saudi workers and the skills that the workers were equipped with from their educational institutions. Planned collaboration between industry and education providers is required to narrow down this gap.

Keywords

Acknowledgement

The authors would like to thank the Deanship of Scientific Research and RSSU at King Saud University for their technical support.

References

  1. ACM. (2017). Task Group on Information Technology Curricula. Information Technology Curricula 2017: Curriculum Guidelines for Baccalaureate Degree Programs in Information Technology. New York, NY, USA: Association for Computing Machinery.
  2. ACT. (2011). A better measure of skills gaps: utilizing ACT skill profile and assessment data for strategic skill research. ACT.
  3. Aken, A., Litecky, C., Ahmad, A., & Nelson, J. (2010). Mining for Computing Jobs. IEEE Software, 27(1), 78-85. https://doi.org/10.1109/MS.2009.150
  4. Al-Khalifa, H. (2017). A survey of IT jobs in the Kingdom of Saudi Arabia 2017. Retrieved December 10, 2020, from https://www.slideshare.net/hend.alkhalifa/a-survey-of-it-jobs-in-the-kingdom-of-saudi-arabia-2017
  5. Alsafadi, L., & Abunafesa, R. (2012). ICT Skills Gap Analysis of the Saudi Market. In The World Congress on Engineering and Computer Science. San Francisco, USA.
  6. Bayt.com. (2020). Retrieved August 1, 2020, from https://www.bayt.com/
  7. Bell, J. (2014). Machine Learning: Hands-On for Developers and Technical Professionals. Wiley.
  8. Bhulai, S. (2016). Analysing which factors are of influence in predicting the employee turnover.
  9. Boone, H. N., & Boone, D. A. (2012). Analyzing Likert data. Journal of Extension.
  10. Brownlee, J. (2018). A Gentle Introduction to k-fold Cross-Validation. Retrieved December 10, 2020, from https://machinelearningmastery.com/k-fold-cross-validation/
  11. Charitable, R. (2011). A Model for Predicting a Career Success in Engineering Among Women and African American Men. ProQuest Dissertations and Theses.
  12. CITC. (2015). ICT workforce in the Kingdom of Saudi Arabia. The Communications and Information Technology Commission (CITC).
  13. Eclipse. (2016). Eclipse. Retrieved from https://eclipse.org/ide/
  14. El-Gabaly, M., & Majidi, M. (2003). The Information Communication Technology (ICT) Penetration and Skills Gap Analysis (SGA). Planning and Learning Inc.
  15. Ericsson, M., & Wingkvist, A. (2014). Mining job ads to find what skills are sought after from an employers' perspective on IT graduates. In the 2014 conference on Innovation & technology in computer science education (ITiCSE '14). Association for Computing Machinery, New York, NY, USA, 354. https://doi.org/https://doi.org/10.1145/2591708.2602670
  16. Gallivan, M., Truex, D., & Kvasny, L. (2002). An analysis of the changing demand patterns for information technology professionals. In the 2002 ACM SIGCPR conference on Computer personnel research (SIGCPR '02). Association for Computing Machinery, New York, NY, USA, 1-13. https://doi.org/https://doi.org/10.1145/512360.512363
  17. Hamoud, B., & Atwell, E. (2016). Quran question and answer corpus for data mining with WEKA. In Proceedings of 2016 Conference of Basic Sciences and Engineering Studies, SGCAC. https://doi.org/10.1109/SGCAC.2016.7458032
  18. Huang, H., Kvasny, L., Joshi, K., Trauth, E., & Mahar, J. (2009). Synthesizing IT job skills identified in academic studies, practitioner publications and job ads. In the special interest group on management information system's 47th annual conference on Computer personnel research (SIGMIS CPR '09). Association for Computing Machinery, New York, NY, USA, 121-128. https://doi.org/https://doi.org/10.1145/1542130.1542154
  19. Ibeaheem, H., Ragmoun, W., & Elawady, S. (2017). The role of Saudi Universities on the improvement of higher education skills on Saudi Arabia. The Business and Management Review, 9(2).
  20. Kennedy, H. (2019). The Labor Market in Saudi Arabia: Background, Areas of Progress, and Insights for the Future. Harvard Kennedy School.
  21. Khan, A., Baharudin, B., Lee, L., & Khan, K. (2010). A Review of Machine Learning Algorithms for Text-Documents Classification. Journal of Advances In Information Technology, 1(1). https://doi.org/10.4304/jait.1.1.4-20
  22. Kilhoffer, Z. (2020). Report on how to identify and compare newly emerging occupations and their skill requirements. Deliverable 12.2, Leuven, InGRID-2 project 730998 - H2020.
  23. Krippendorff, K. (2018). Content Analysis: An Introduction to Its Methodology (FOURTH EDI). SAGE Publications, Inc.
  24. Labor market reports. (2020). Jadwa Investment.
  25. Lending, D., & Dillon, T. (2013). Identifying skills for entry-level IT consultants. In the 2013 annual conference on Computers and people research (SIGMIS-CPR '13). Association for Computing Machinery, New York, NY, USA, 87-92. https://doi.org/https://doi.org/10.1145/2487294.2487311
  26. Linkedin. (2020). Retrieved August 1, 2020, from https://www.linkedin.com/
  27. MCIT. (2014). IDC: Saudi IT Spending To Reach $14.2bn In 2017. Retrieved July 3, 2020, from available: https://www.mcit.gov.sa/en/media-center/news/92185
  28. Mishrif, A., & Alabduljabbar, A. (2018). Quality of Education and Labour Market in Saudi Arabia. In: Mishrif A., Al Balushi Y. (Eds) Economic Diversification in the Gulf Region, 1(The Political Economy of the Middle East. Palgrave Macmillan, Singapore). https://doi.org/https://doi.org/10.1007/978-981-10-5783-0_5
  29. Octoparse. (2020). Retrieved November 10, 2020, from https://www.octoparse.com/
  30. OpenRefine. (2020). OpenRefine Tool. Retrieved October 10, 2020, from https://openrefine.org/
  31. Paparrizos, I., Cambazoglu, B., & Gionis, A. (2011). Machine learned job recommendation. In the fifth ACM conference on Recommender systems (RecSys '11). Association for Computing Machinery, New York, NY, USA (pp. 325-328). https://doi.org/https://doi.org/10.1145/2043932.2043994
  32. Punnoose, R., & Ajit, P. (2016). Prediction of Employee Turnover in Organizations using Machine Learning Algorithms. International Journal of Advanced Research in Artificial Intelligence(IJARAI), 5(9). https://doi.org/http://dx.doi.org/10.14569/IJARAI.2016.050904
  33. Sen, S. (2011). MANAGERIAL PERSPECTIVES OF INFORMATION TECHNOLOGY SKILLS FOR COMPUTER TRAINING INSTITUTES. International Journal of Arts & Sciences, 4(12), 267-282.
  34. SSDS. (2010). Numeracy Skills.
  35. Stevens, M., & Norman, R. (2016). Industry expectations of soft skills in IT graduates: a regional survey. In the Australasian Computer Science Week Multiconference (ACSW '16). Association for Computing Machinery, New York, NY, USA, Article 13, 1-9. https://doi.org/https://doi.org/10.1145/2843043.2843068
  36. Tapado, B., Acedo, G., & Palaoag, T. (2018). Evaluating information technology graduates employability using decision tree algorithm. In the 9th International Conference on E-Education, E-Business, E-Management and E-Learning (pp. 88-93).
  37. Waikato. (2016). Weka 3 - Data Mining with Open Source Machine Learning Software in Java. The University of Waikato.
  38. Wowczko, I. (2015). Skills and Vacancy Analysis with Data Mining Techniques. Informatics, 2(4), 31-49. https://doi.org/https://doi.org/10.3390/informatics2040031
  39. Zhu, C., Zhu, H., Xiong, H., Ma, C., Xie, F., Pengliang, D., & Li, P. (2018). Person-Job Fit: Adapting the Right Talent for the Right Job with Joint Representation Learning. ACM Transactions on Management Information Systems, 9(3), 17 pages. https://doi.org/https://doi.org/10.1145/3234465