PREDICTING EMPLOYMENT OUTCOMES OF AUSTRALIAN INFORMATION TECHNOLOGY GRADUATES USING MACHINE LEARNING
Keywords:
graduate employability, information technology, machine learning, higher education, employment forecasting, AustraliaAbstract
Graduate employment outcomes in information technology have become a major issue for Australian higher education because the strong growth in digital work is not necessarily translating into immediate professional employment for new graduates. In this study, we present a machine learning prediction of the overall employment outcomes for Australian Computing and Information Systems graduates based on the latest publicly available national evidence. We constructed a longitudinal panel from Quality Indicators for Learning and Teaching Graduate Outcomes Survey data of multiple undergraduate study areas over the last few years. We also modelled full time employment, overall employment, participation and median salary using regularised regression with study area specific time effects and an indicator that accounts for changes in survey definitions. Random forest and gradient boosting models were used as prediction models for time block validation. The results show a weakening in immediate full-time employment among Computing and Information Systems graduates in the face of an expanding graduate salary base and continued demand for the most demanding ICT fields. Our forecasts indicate that competitive entry is likely to remain on a strong footing in the medium term and that the graduate salary improves. These results demonstrate an ongoing disconnect between demand for jobs and graduate transition outcomes, and this has implications for industry engagement, work integrated learning, authentic assessment, career development and data informed employability support in Australian higher education.
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Aviso, K. B., Demeterio III, F. P. A., Janairo, J. I. B., Lucas, R. I. G., Promentilla, M. A. B., Tan, R. R., & Yu, D. E. C. (2021). What university attributes predict for graduate employability? Cleaner Engineering and Technology, 2, 100069. https://doi.org/10.1016/j.clet.2021.100069
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82-115. https://doi.org/10.1016/j.inffus.2019.12.012
Clarke, M. (2018). Rethinking graduate employability: The role of capital, individual attributes and context. Studies in Higher Education, 43(11), 1923-1937. https://doi.org/10.1080/03075079.2017.1294152
Donald, W. E., Baruch, Y., & Ashleigh, M. (2019). The undergraduate self-perception of employability: Human capital, careers advice, and career ownership. Studies in Higher Education, 44(4), 599-614. https://doi.org/10.1080/03075079.2017.1387107
Hains-Wesson, R., & Ji, K. (2020). Students' perceptions of an interdisciplinary global study tour: Uncovering inexplicit employability skills. Higher Education Research & Development, 39(4), 657-671. https://doi.org/10.1080/07294360.2019.1695752
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 and Information Technology, 14(1), 45-56. https://doi.org/10.18517/ijaseit.14.1.19549
Herbert, I. P., Rothwell, A. T., Glover, J. L., & Lambert, S. A. (2020). Graduate employability, employment prospects and work-readiness in the changing field of professional work. The International Journal of Management Education, 18(2), 100378. https://doi.org/10.1016/j.ijme.2020.100378
Jackson, D. (2020). Applying academic selection criterion to work-integrated learning programmes: Risk management or perpetuating inequality? Teaching in Higher Education, 25(1), 98-115. https://doi.org/10.1080/13562517.2018.1541884
Jackson, D. (2024). Work-integrated learning: Opportunities and challenges in Australia. Higher Education Research & Development, 43(3), 767-773. https://doi.org/10.1080/07294360.2024.2307929
Jackson, D., & Bridgstock, R. (2021). What actually works to enhance graduate employability? The relative value of curricular, co-curricular, and extra-curricular learning and paid work. Higher Education, 81(4), 723-739. https://doi.org/10.1007/s10734-020-00570-x
Jackson, D., & Dean, B. A. (2023). The contribution of different types of work-integrated learning to graduate employability. Higher Education Research & Development, 42(1), 93-110. https://doi.org/10.1080/07294360.2022.2048638
Jackson, D., & Meek, S. (2021). Embedding work-integrated learning into accounting education: The state of play and pathways to future implementation. Accounting Education, 30(1), 63-85. https://doi.org/10.1080/09639284.2020.1794917
Jackson, D., & O'Connor, M. D. (2026). Curriculum approaches to strengthening student employability and improving graduate outcomes in the STM disciplines. International Journal of Science Education, 48(4), 535-558. https://doi.org/10.1080/09500693.2024.2419393
Jackson, D., & Rowe, A. (2023). Impact of work-integrated learning and co-curricular activities on graduate labour force outcomes. Studies in Higher Education, 48(3), 490-506. https://doi.org/10.1080/03075079.2022.2145465
Jackson, D., Lambert, C., Sibson, R., Bridgstock, R., & Tofa, M. (2024). Student employability-building activities: Participation and contribution to graduate outcomes. Higher Education Research & Development, 43(6), 1308-1324. https://doi.org/10.1080/07294360.2024.2325154
Jobs and Skills Australia. (2025). Cyber security skills in demand as labour market evolves. Australian Government.
Jobs and Skills Australia. (2026). Occupation and industry profiles. Australian Government.
Kahlik, A. A., & Saleh Al-Hababi, A. Y. (2024). Predicting post-internship employability using ensemble machine learning approach. Journal of Cognitive Sciences and Human Development, 10(2), 87-101. https://doi.org/10.33736/jcshd.7518.2024
Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020). From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2(1), 56-67. https://doi.org/10.1038/s42256-019-0138-9
Mewburn, I., Grant, W. J., Suominen, H., & Kizimchuk, S. (2020). A machine learning analysis of the non-academic employment opportunities for Ph.D. graduates in Australia. Higher Education Policy, 33(4), 799-813. https://doi.org/10.1057/s41307-018-0098-4
Oliver, B., & Jorre de St Jorre, T. (2018). Graduate attributes for 2020 and beyond: Recommendations for Australian higher education providers. Higher Education Research & Development, 37(4), 821-836. https://doi.org/10.1080/07294360.2018.1446415
Probst, P., Wright, M. N., & Boulesteix, A.-L. (2019). Hyperparameters and tuning strategies for random forest. WIREs Data Mining and Knowledge Discovery, 9(3), e1301. https://doi.org/10.1002/widm.1301
Römgens, I., Scoupe, R., & Beausaert, S. (2020). Unraveling the concept of employability, bringing together research on employability in higher education and the workplace. Studies in Higher Education, 45(12), 2588-2603. https://doi.org/10.1080/03075079.2019.1623770
Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2, 160. https://doi.org/10.1007/s42979-021-00592-x
Small, L., Shacklock, K., & Marchant, T. (2018). Employability: A contemporary review for higher education stakeholders. Journal of Vocational Education & Training, 70(1), 148-166. https://doi.org/10.1080/13636820.2017.1394355
Social Research Centre. (2023). 2022 Graduate Outcomes Survey - National Report. Social Research Centre.
Social Research Centre. (2025). 2024 Graduate Outcomes Survey - National Report. Social Research Centre.
Social Research Centre. (2026a). 2025 Graduate Outcomes Survey - National Report. Social Research Centre.
Social Research Centre. (2026b). 2025 Employer Satisfaction Survey - National Report. Social Research Centre.
Succi, C., & Canovi, M. (2020). Soft skills to enhance graduate employability: Comparing students and employers' perceptions. Studies in Higher Education, 45(9), 1834-1847. https://doi.org/10.1080/03075079.2019.1585420
Tomlinson, M. (2017). Forms of graduate capital and their relationship to graduate employability. Education + Training, 59(4), 338-352. https://doi.org/10.1108/ET-05-2016-0090
