Predicting Employment Outcomes of Australian Information Technology Graduates Using Machine Learning
DOI:
https://doi.org/10.56404/jels.v6i2.487Keywords:
employment forecasting, graduate employability, higher education, information technology, machine learningAbstract
Graduate employment outcomes in information technology are an important issue for Australian higher education because growth in digital work has translated unevenly into immediate professional employment for new graduates. This study predicts aggregate employment outcomes for Australian Computing and Information Systems graduates using the latest publicly available national evidence. A longitudinal panel was constructed from Quality Indicators for Learning and Teaching Graduate Outcomes Survey data covering multiple undergraduate study areas. Full-time employment, overall employment, participation and median salary were modelled using regularised regression with study-area-specific time effects and an indicator for changes in survey definitions, while random forest and gradient boosting models provided time-block validation benchmarks. Results show weakening immediate full-time employment alongside rising graduate salaries and continuing demand across major ICT occupations. Forecasts indicate continued competitive entry to full-time professional work over the medium term while median graduate salary increases. The findings highlight a persistent disconnect between occupational demand and graduate transition outcomes, with implications for industry engagement, work-integrated learning, authentic assessment, career development and data-informed employability support in Australian higher education.
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