Development of a Graduate Employability Dashboard for Community Colleges

Authors

Muhammad Farhan Bin Hj Azmir

Faculty of Industrial Management, Universiti Malaysia Pahang Al-Sultan Abdullah (Malaysia)

Muhammad Abdul Adib Bin Abd Aziz

Faculty of Industrial Management, Universiti Malaysia Pahang Al-Sultan Abdullah (Malaysia)

Mohd Ruzaimi Bin Mohd Ariffin

Faculty of Industrial Management, Universiti Malaysia Pahang Al-Sultan Abdullah (Malaysia)

Article Information

DOI: 10.47772/IJRISS.2026.1026EDU0361

Subject Category: AI

Volume/Issue: 10/26 | Page No: 4631-4665

Publication Timeline

Submitted: 2026-05-20

Accepted: 2026-05-25

Published: 2026-06-19

Abstract

Sistem Kajian Pengesanan Graduan (SKPG), also known as the Graduate Tracer Study, is a structured survey implemented by Malaysia’s Ministry of Higher Education (MOHE) to monitor the employment outcomes of graduates from higher education institutions. Graduate Tracer Studies (GTS) serve as an important mechanism for examining the relationship between higher education and labour market needs. These studies provide insights into graduates’ employment status, job relevance, career progression, and level of satisfaction with the programmes and institutions they attended. The implementation of GTS involves several key stages, including study design, identification of data sources, selection of the target population, determination of the appropriate survey period, and the development of comprehensive questionnaires. Data are collected by engaging graduates directly, and the findings are subsequently analysed and presented to support institutional planning, curriculum improvement, policy development, and graduate employability initiatives. This project is designed to share relevant international expertise and best practices in Graduate Tracer Studies with Jabatan Pendidikan Politeknik dan Kolej Komuniti (JPPKK), Malaysia. In addition, the integration of Machine Learning (ML) into graduate tracer study analysis can enhance the prediction of graduate employability rates. By using ML techniques, educational institutions can gain deeper insights into the effectiveness of their programmes and make more informed decisions to align academic offerings with students’ needs and labour market demands.

Keywords

Graduate Employability; Graduate Tracer Study; Machine Learning; Predictive Analytics; Employability Dashboard

Downloads

References

1. Ab Rahman, M. S. (2023), Tiga Faktor Kebolehpasaran Graduan-Pascapandemik, Kosmo, retrieved from https://www.kosmo.com.my/2023/11/29/tiga-faktor-kebolehpasaran-graduan-pascapandemik/ [Google Scholar] [Crossref]

2. ElSharkawy, G., Helmy, Y., and Yehia, E. (2022), Employability Prediction of Information Technology Graduates using Machine Learning Algorithms, International Journal of Advanced Computer Science and Applications, (IJACSA), Vol. 13, No. 10, 2022 359 | P a g e, www.ijacsa.thesai.org [Google Scholar] [Crossref]

3. Fuad, F. (2020), Kebolehpasaran Graduan TVET Capai 95 Peratus, Berita Harian, retrieved from https://www.bharian.com.my/berita/nasional/2020/11/759851/ kebolehpasaran-graduan-tvet-capai-95-peratus/ [Google Scholar] [Crossref]

4. Haque, R., Quek, A., Ting, C. Y., Goh, H. N., & Hasan, M. R. (2024), Classification Techniques Using Machine Learning for Graduate Student Employability Predictions. International Journal on Advanced Science, Engineering & Information Technology, 14(1). [Google Scholar] [Crossref]

5. Ibrahim, A., (2023), Kebolehpasaran Graduan Satu Cabaran Nasional, Utusan Malaysia, retrieved from https://www.utusan.com.my/nasional/2023/08/ kebolehpasaran-graduan-satu-cabaran-nasional/ [Google Scholar] [Crossref]

6. Jabatan Penerangan Malaysia (2024), Gaji premium lepasan TVET ditetapkan hingga RM4,000, Jabatan Penerangan Malaysia, retrieved from https://www.penerangan.gov.my/gaji-premium-lepasan-tvet-ditetapkan-pada-kadar-rm2500-hingga-rm4000/ [Google Scholar] [Crossref]

7. Jabarullah, N. H. and Iqbal Hussain, H. (2019), The Effectiveness of Problem-Based Learning in Technical and Vocational Education in Malaysia, Education + Training, Vol. 61 No. 5, pp. 552-567. https://doi.org/10.1108/ET-06-2018-0129 [Google Scholar] [Crossref]

8. Shahriyar, J., Ahmad, J. B., Zakaria, N. H. and Su, G. E. (2022), Enhancing Prediction of Employability of Students: Automated Machine Learning Approach, 2022 2nd International Conference on Intelligent Cybernetics Technology & Applications (ICICyTA), Bandung, Indonesia, pp. 87-92, doi: 10.1109/ICICyTA57421.2022.10038231. [Google Scholar] [Crossref]

9. MDEC (Malaysia Digital Economy Corporation). (2021). Annual Report 2021. [Google Scholar] [Crossref]

10. Ministry of Higher Education Malaysia. (2021). Laporan Kajian Pengesanan Graduan 2021. Ministry of Higher Education Malaysia. [Google Scholar] [Crossref]

11. Ministry of Higher Education Malaysia. (2022). Laporan Kajian Pengesanan Graduan 2022. Ministry of Higher Education Malaysia. [Google Scholar] [Crossref]

12. Ministry of Higher Education Malaysia. (2023). Laporan Kajian Pengesanan Graduan 2023. Ministry of Higher Education Malaysia. [Google Scholar] [Crossref]

13. Ministry of Higher Education Malaysia. (2020). Pelan Strategik Kebolehpasaran Graduan KPT 2021-2025. Ministry of Higher Education Malaysia. [Google Scholar] [Crossref]

14. Ministry of Education Malaysia. (2019). Technical and Vocational Education and Training (TVET) Strategy 2018-2025. Ministry of Education Malaysia. [Google Scholar] [Crossref]

15. Raman,R. and Pramod, D. (2021), The role of predictive analytics to explain the employability of management graduates, Emerald Insight, https://www.emerald.com/insight/1463-5771.htm [Google Scholar] [Crossref]

16. Sapaat, M. A., Mustapha, A., Ahmad, J., Chamili, K. and Muhamad, R. (2011). A Data Mining Approach to Construct Graduates Employability Model in Malaysia. International Journal of New Computer Architectures and their Applications (IJNCAA). 4. 1111-1124. [Google Scholar] [Crossref]

17. Tableau Software. (2020). Best Practices for Creating Effective Dashboards. Available at: Tableau. [Google Scholar] [Crossref]

18. UNESCO. (2016). Strategy for Technical and Vocational Education and Training (TVET) 2016-2021. UNESCO. [Google Scholar] [Crossref]

19. Vinutha, K. and Yogisha, H. K. (2021), Prediction of Employability of Engineering Graduates using Machine Learning Techniques, 2021 8th International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India, pp. 742-745. [Google Scholar] [Crossref]

20. Zheng D. (2023), Simulation Research on College Students’ Employment Prediction Model Based on Decision Tree Classification Algorithm, 2023 International Conference on Internet of Things, Robotics and Distributed Computing (ICIRDC), Rio De Janeiro, Brazil, pp. 194-199, doi: 10.1109/ICIRDC62824.2023.00041. [Google Scholar] [Crossref]

Metrics

Views & Downloads

Similar Articles